Cross-scale modeling of bioreactor cultures using raman spectroscopy

ABSTRACT

Aspect of the disclosure relate to methods of assessing a bioreactor culture that involve determining a culture parameter of the manufacturing-scale bioreactor culture using a model that relates a Raman spectrum to the culture parameter. Related bioreactor system are also provided.

RELATED APPLICATIONS

This application claims the benefit under 35 U.S.C. § 119(e) of U.S.Provisional Application No. 62/020371, entitled “CROSS-SCALE MODELING OFBIOREACTOR CULTURES USING RAMAN SPECTROSCOPY”, filed Jul. 2, 2014, thecontents of which are incorporated herein by reference in its entirety.

BACKGROUND OF INVENTION

In recent years the United States Food and Drug Administration (FDA) andthe European Medicines Agency (EMEA) have emphasized a desire to seegreater control and process understanding in the area of pharmaceuticaldevelopment, manufacturing and quality assurance which is collectivelyreferred to as process analytical technology (PAT). Likewise,bioprocessing and specifically cell culture application users areinterested in attaining more advanced, informative and significantreal-time data out of their systems to drive process development andmanufacturing success while also ushering in a generation of advancedprocess control (APC).

While the real-time monitoring of pH, dissolved oxygen (DO), dissolvedCO2 and temperature are relevant to cell culture applications and havebeen typical online monitoring and control parameters within cellculture and bioprocessing applications for decades, there are also otherprocess parameters that involve manual bioreactor sampling and offlineanalysis. These parameters may include total cell density (TCD) and,viable cell density (TCD), protein production and cell metabolismcomponents including amino acids, growth nutrients, and cell wasteproducts. A need exists to transfer the monitoring of thesetraditionally offline measured parameters towards an in situ applicationwhich will create the potential for real-time control. Driving thistransition to PAT and APC while also meeting the guidance provided inthe regulatory framework poses specific challenges for the development,manufacturing and quality assurance of biologics due to the multivariatenature of the environments required for synthesizing these complexprotein structures.

SUMMARY OF INVENTION

Aspects of the disclosure relate to methods of monitoring and/orassessing bioreactor cultures using Raman spectroscopy. In particular,methods are provided for assessing culture parameters (e.g., the levelof a component of a bioreactor culture) using scale independentmultivariate models developed based on Raman spectral data obtained frombioreactor cultures of one or more different scales. In someembodiments, methods provided herein involve multivariate models basedon Raman spectral data obtained at test-scales (e.g., bench and/orpilot-scales) that are accurate at larger manufacturing-scale (e.g.,1000 L or greater) settings. Thus, in some embodiments, multivariatemodels provided herein have the ability to predict process performancein commercial manufacturing based on data from scaled down bioreactormodels. Smaller development scale test reactors, which may includebench-scale and/or pilot-scale reactors, are advantageous because theyallow more parameters to be tested for a given cost. However, in someembodiments, methods provided herein involve multivariate models basedon Raman spectral data obtained across multiple different scales ofbioreactor cultures, including, for example, bench and/or pilot-scales,that are accurate across a range of scales, includingmanufacturing-scales. In some embodiments, by gathering data at bench,pilot and/or manufacturing-scales, models incorporate tremendous processvariability, offering a great deal of robustness to the final model.

Accordingly, aspects of the disclosure relate to methods of monitoringand/or assessing a bioreactor culture. In some embodiments, the methodsinvolve (i) obtaining a Raman spectrum of a manufacturing-scalebioreactor culture; and (ii) determining a culture parameter of themanufacturing-scale bioreactor culture using a model that relates theRaman spectrum to the culture parameter, in which the model is developedbased on one or more test bioreactor cultures of a smaller volume thanthe manufacturing-scale bioreactor culture. In some embodiments, thevolume of the manufacturing-scale bioreactor culture is in a range of1000 L to 4000 L. In some embodiments, the volume of the test bioreactorculture is in a range of 1 L to 400 L. In some embodiments, the testbioreactor culture is a bench-scale bioreactor culture. In someembodiments, the bench-scale bioreactor culture is in a range of 1 L to5 L. In some embodiments, the test bioreactor culture is a pilot-scalebioreactor culture. In some embodiments, the pilot-scale bioreactorculture is in a range of 50 L to 100 L.

In some embodiments of the methods, the model is a multivariate model,such as a partial least squares model. In some embodiments, the cultureparameter is a level of glucose, glutamate, ammonia or lactate in theculture. In some embodiments, the culture parameter is a level ofglucose, and the model has: (i) a root mean square error of estimationin a range of 0.50 g/L to 1 g/L, and/or (ii) a root mean square error ofcross validation in a range of 0.50 g/L to 1 g/L, and/or (iii) a rootmean square error of prediction in a range of 0.50 g/L to 1.5 g/L,and/or (iv) an average percentage error of up to 10%. In someembodiments, the culture parameter is a level of lactate, and the modelhas: (i) a root mean square error of estimation in a range of 0.10 g/Lto 0.20 g/L, and/or (ii) a root mean square error of cross validation ina range of 0.10 g/L to 0.20 g/L, and/or (iii) a root mean square errorof prediction in a range of 0.10 g/L to 0.20 g/L, and/or (iv) an averagepercentage error of up to 20%. In some embodiments, the cultureparameter is a level of glutamate, and the model has: (i) a root meansquare error of estimation in a range of 0.10 mM to 0.20 mM, and/or (ii)a root mean square error of cross validation in a range of 0.10 mM to0.40 mM, and/or (iii) a root mean square error of prediction in a rangeof 0.40 mM to 1.5 mM, and/or (iv) an average percentage error of up to35%. In some embodiments, the culture parameter is a level of ammonium,and the model has: (i) a root mean square error of estimation in a rangeof 0.20 mM to 0.40 mM, and/or (ii) a root mean square error of crossvalidation in a range of 0.20 mM to 0.50 mM, and/or (iii) a root meansquare error of prediction in a range of 0.40 mM to 1.5 mM, and/or (iv)an average percentage error of up to 20%.

In some embodiments of the methods, the culture parameter is theosmolality of the culture. In some embodiments, the culture parameter isthe osmolality of the culture and the model has: (i) a root mean squareerror of estimation in a range of 5 mOsm/kg to 15 mOsm/kg, and/or (ii) aroot mean square error of cross validation in a range of 10 mOsm/kg to15 mOsm/kg, and/or (iii) a root mean square error of prediction in arange of 10 mOsm/kg to 25 mOsm/kg, and/or (iv) an average percentageerror of up to 10%.

In some embodiments, methods provided herein of monitoring and/orassessing a bioreactor culture involve (i) obtaining a Raman spectrum ofa first bioreactor culture of a first volume; and (ii) determining aculture parameter of the first bioreactor culture using a model,developed based on a second bioreactor culture of a second volume, thatrelates the Raman spectrum to the culture parameter. In someembodiments, the second volume is in a range of 0.0005% to 90% or0.0005% to 50% of first volume. In some embodiments, the second volumeis in a range of 0.1% to 10% of first volume.

In some embodiments, methods provided herein of monitoring and/orassessing a bioreactor culture involve (i) obtaining a Raman spectrum ofa manufacturing-scale bioreactor culture and (ii) determining a cultureparameter of the manufacturing-scale bioreactor culture using a modelthat relates the Raman spectrum to the culture parameter, wherein themodel is developed based on at least one bioreactor culture of a smallervolume than the manufacturing-scale bioreactor culture and at least onebioreactor culture of substantially the same volume as themanufacturing-scale bioreactor culture. In some embodiments, themanufacturing-scale bioreactor culture is in a range of 1000 L to 4000L. In some embodiments, the at least one bioreactor culture of a smallervolume than the manufacturing-scale bioreactor culture is in a range of1 L to 100 L.

In some embodiments of methods disclosed herein, a Raman spectrumcomprises spectral signal in the 200 cm⁻¹ to 3400 cm⁻¹ wavenumber range.In some embodiments, a Raman spectrum comprises spectral signal in thevisible, near infrared, infrared, near ultraviolet, or ultraviolet (UV)range. In some embodiments, a Raman spectrum is obtained using SurfaceEnhanced Raman Spectroscopy (SERS), resonance Raman spectroscopy,tip-enhanced Raman spectroscopy, polarized Raman spectroscopy,stimulated Raman spectroscopy, transmission Raman spectroscopy,spatially offset Raman spectroscopy, difference Raman spectroscopy,Fourier Transform (FT) Raman, or hyper Raman spectroscopy. In someembodiments, a Raman spectrum is obtained using a Raman analyzerconfigured with a laser or other suitable light source that operates atwavelengths in a range of 325 nm to 1064 nm.

Aspects of the disclosure relate to bioreactor systems. In someembodiments, the bioreactor systems comprise: a bioreactor chamberconfigured for containing a bioreactor culture (e.g., amanufacturing-scale bioreactor culture) and a probe configured forobtaining a Raman spectrum of the bioreactor culture. In someembodiments, bioreactor systems disclosed herein further comprises acomputer configured for determining a culture parameter of a bioreactorculture (e.g., a manufacturing-scale bioreactor culture). In someembodiments, a computer of a bioreactor system comprises an inputinterface configured to receive information indicative of a Ramanspectrum obtained from the probe. In some embodiments, a computer of abioreactor system further comprises at least one processor programmed toevaluate a model that relates a Raman spectrum (e.g., obtained from aRaman probe) to a culture parameter. In some embodiments, the model isdeveloped based on one or more test bioreactor cultures of a smallervolume than the manufacturing-scale bioreactor culture. In someembodiments, a computer further comprises an output interface configuredto output a signal indicative of the determined culture parameter. Insome embodiments, the output comprises a feedback control signal forcontrolling operation of a device for altering the culture parameter. Insome embodiments, the device for altering the culture parameter is apump or valve that controls flow, into or out from the bioreactorculture, of a medium comprising one or more culture components.

Aspects of the disclosure relate to bioreactor systems that comprises: abioreactor chamber configured for containing a manufacturing-scalebioreactor culture; a probe configured for obtaining a Raman spectrum ofthe manufacturing-scale bioreactor culture; and a computer configuredfor determining a culture parameter of the manufacturing-scalebioreactor culture, in which the computer comprises: an input interfaceconfigured to receive information indicative of the Raman spectrumobtained from the probe; at least one processor programmed to evaluate amodel that relates the Raman spectrum to the culture parameter, in whichthe model is developed based on at least one bioreactor culture of asmaller volume than the manufacturing-scale bioreactor culture and atleast one bioreactor culture of substantially the same volume as themanufacturing-scale bioreactor culture an output interface configured tooutput a signal indicative of the determined culture parameter.

In some embodiments of bioreactor systems disclosed herein, the volumeof the manufacturing-scale bioreactor culture is in a range of 1000 L to4000 L. In some embodiments, the volume of the test bioreactor cultureis in a range of 1 L to 400 L. In some embodiments, the test bioreactorculture is a bench-scale bioreactor culture. In some embodiments, thebench-scale bioreactor culture is in a range of 1 L to 5 L. In someembodiments, the test bioreactor culture is a pilot-scale bioreactorculture. In some embodiments, the pilot-scale bioreactor culture is in arange of 50 L to 100 L. In some embodiments, the model is a multivariatemodel, such as a partial least squares model. In some embodiments, theculture parameter is a level of glucose, glutamate, ammonia or lactatein the culture. In some embodiments, the culture parameter is a level ofglucose, and the model has: (i) a root mean square error of estimationin a range of 0.50 g/L to 1 g/L, and/or (ii) a root mean square error ofcross validation in a range of 0.50 g/L to 1 g/L, and/or (iii) a rootmean square error of prediction in a range of 0.50 g/L to 1.5 g/L,and/or (iv) an average percentage error of up to 10%. In someembodiments, the culture parameter is a level of lactate, and the modelhas: (i) a root mean square error of estimation in a range of 0.10 g/Lto 0.20 g/L, and/or (ii) a root mean square error of cross validation ina range of 0.10 g/L to 0.20 g/L, and/or (iii) a root mean square errorof prediction in a range of 0.10 g/L to 0.20 g/L, and/or (iv) an averagepercentage error of up to 20%. In some embodiments, the cultureparameter is a level of glutamate, and the model has (i) a root meansquare error of estimation in a range of 0.10 mM to 0.20 mM, and/or (ii)a root mean square error of cross validation in a range of 0.10 mM to0.40 mM, and/or (iii) a root mean square error of prediction in a rangeof 0.40 mM to 1.5 mM, and/or (iv) an average percentage error of up to35%. In some embodiments, the culture parameter is a level of ammonium,and the model has: (i) a root mean square error of estimation in a rangeof 0.20 mM to 0.40 mM, and/or (ii) a root mean square error of crossvalidation in a range of 0.20 mM to 0.50 mM, and/or (iii) a root meansquare error of prediction in a range of 0.40 mM to 1.5 mM, and/or (iv)an average percentage error of up to 20%. In some embodiments, theculture parameter is the osmolality of the culture. In some embodiments,the culture parameter is the osmolality of the culture and the modelhas: (i) a root mean square error of estimation in a range of 5 mOsm/kgto 15 mOsm/kg, and/or (ii) a root mean square error of cross validationin a range of 10 mOsm/kg to 15 mOsm/kg, and/or (iii) a root mean squareerror of prediction in a range of 10 mOsm/kg to 25 mOsm/kg, and/or (iv)an average percentage error of up to 10%.

In some embodiments of the bioreactor systems disclosed herein, a Ramanspectrum comprises spectral signal in the visible, near infrared,infrared, near ultraviolet, or ultraviolet (UV) range. In someembodiments, the Raman spectrum is obtained using Surface Enhanced RamanSpectroscopy (SERS), resonance Raman spectroscopy, tip-enhanced Ramanspectroscopy, polarized Raman spectroscopy, stimulated Ramanspectroscopy, transmission Raman spectroscopy, spatially offset Ramanspectroscopy, difference Raman spectroscopy, Fourier Transform (FT)Fourier Transform (FT) Raman, or hyper Raman spectroscopy. In someembodiments, the probe comprises a Raman analyzer configured with alaser or other suitable light source that operates at wavelengths in arange of 325 nm to 1064 nm.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is a non-limiting example of a process flow diagram for datapreprocessing, multivariate model building, optimization, and selectioncriteria of PLS models;

FIG. 2A is a non-limiting example of a manufacturing-scale time coursespectra shaded by batch elapsed time (raw);

FIG. 2B is a non-limiting example of a manufacturing-scale time coursespectra shaded by batch elapsed time (preprocessed with 1st derivativeonly);

FIG. 2C is a non-limiting example of a manufacturing-scale time coursespectra shaded by batch elapsed time (preprocessed with 1st derivative,Savitzky-Golay and SNV);

FIG. 3A is a non-limiting example of PLS model prediction results,predicting a single 2,000 L manufacturing-scale validation batch forVCD;

FIG. 3B is a non-limiting example of PLS model prediction results,predicting a single 2,000 L manufacturing-scale validation batch forTCD;

FIG. 3C is a non-limiting example of PLS model prediction results,predicting a single 2,000 L manufacturing-scale validation batch forglucose;

FIG. 3D is a non-limiting example of PLS model prediction results,predicting a single 2,000 L manufacturing-scale validation batchlactate;

FIG. 3E is a non-limiting example of PLS model prediction results,predicting a single 2,000 L manufacturing-scale validation batch forglutamate;

FIG. 3F is a non-limiting example of PLS model prediction results,predicting a single 2,000 L manufacturing-scale validation batch forammonium;

FIG. 3G is a non-limiting example of PLS model prediction results,predicting a single 2,000 L manufacturing-scale validation batchosmolality;

FIG. 4 is a non-limiting example of a root mean squared error ofprediction (RMSEP) across various single-scale and combination-scalemodels;

FIG. 5A is a non-limiting example of a calibration model of actual(offline reference data) versus predicted (PLS model generated) valuesacross various culture processing scales for VCD;

FIG. 5B is a non-limiting example of a calibration model of actual(offline reference data) versus predicted (PLS model generated) valuesacross various culture processing scales for TCD;

FIG. 5C is a non-limiting example of a calibration model of actual(offline reference data) versus predicted (PLS model generated) valuesacross various culture processing scales for glucose;

FIG. 5D is a is a non-limiting example of a calibration model of actual(offline reference data) versus predicted (PLS model generated) valuesacross various culture processing scales for lactate;

FIG. 5E is a non-limiting example of a calibration model of actual(offline reference data) versus predicted (PLS model generated) valuesacross various culture processing scales for glutamate;

FIG. 5F is a non-limiting example of a calibration model of actual(offline reference data) versus predicted (PLS model generated) valuesacross various culture processing scales for ammonium;

FIG. 5G is a non-limiting example of a calibration model of actual(offline reference data) versus predicted (PLS model generated) valuesacross various culture processing scales for osmolality;

FIG. 6A is a non-limiting example of a raw Raman spectra of sodiumL-lactate solution in chemically defined cell culture media zoomed in atthe regions between 420-1600 wavenumber (cm-1), the following peaks areannotated: Peakl (500-575 cm-1), Peak 2 (820-880 cm-1), Peak 3 (900-950cm-1), Peak 4 (1010-1110 cm-1), Peak 5 (1290-1340 cm-1), and Peak 6(1360-1510 cm-1);

FIG. 6B is a non-limiting example of a signal processed and normalizedRaman spectra of sodium L-lactate solution in chemically defined cellculture media zoomed in at the regions between 420-1600 wavenumber(cm-1), the following peaks are annotated: Peakl (500-575 cm-1), Peak 2(820-880 cm-1), Peak 3 (900-950 cm-1), Peak 4 (1010-1110 cm-1), Peak 5(1290-1340 cm-1), and Peak 6 (1360-1510 cm-1);

FIG. 6C is a non-limiting example of a raw Raman spectra of sodiumL-lactate solution in chemically defined cell culture media zoomed in atthe regions between & 2750-3400 wavenumber (cm-1), the following peak isannotated: Peak 7 (2840-3020 cm-1); and

FIG. 6D is a is a non-limiting example of a signal processed andnormalized Raman spectra of sodium L-lactate solution in chemicallydefined cell culture media zoomed in at the regions between 2750-3400wavenumber (cm-1), the following peak is annotated: Peak 7 (2840-3020cm-1).

DETAILED DESCRIPTION OF INVENTION

Aspects of the disclosure relate to methods of monitoring and/orassessing bioreactor cultures using Raman spectroscopy. In particular,methods are provided for assessing culture parameters (e.g., the levelof a component of a bioreactor culture) using scale independentmultivariate models developed based on Raman spectral data. In someembodiments, methods provided herein involve multivariate models basedon Raman spectral data obtained at one or more different scales that areaccurate at larger manufacturing-scale (e.g., 1000 L or greater)settings. In some embodiments, methods provided herein involve obtaininga Raman spectrum of a first bioreactor culture of a first volume (e.g.,a manufacturing-scale) and determining a culture parameter of the firstbioreactor culture using a model, developed based on a second bioreactorculture of a second volume (e.g., a bench or pilot-scale), that relatesthe Raman spectrum to the culture parameter, wherein the second volumeis smaller than the first volume. For example, the second volume may bethe volume of a developmental scale bioreactor, such as a laboratory(e.g., bench top scale bioreactor) or a pilot-scale bioreactor. In someembodiments, the second volume is in a range of 0.0005% to 50% of firstvolume. In some embodiments, methods provided herein involve obtaining aRaman spectrum of a manufacturing-scale bioreactor culture anddetermining a culture parameter of the manufacturing-scale bioreactorculture using a model that relates the Raman spectrum to the cultureparameter, in which the model is developed based on one or more testbioreactor cultures of a smaller volume than the manufacturing-scalebioreactor culture.

Bioreactor Cultures and Components Thereof

Generally, a bioreactor refers to an engineered device that supports oris capable of supporting cell culture, e.g., for the production of atherapeutic agent by cells of the culture. For example, a bioreactor maycomprise a vessel in which a chemical process is carried out thatinvolves organisms, cells or biochemically active substances derivedfrom such organisms. While many bioreactors are made primarily ofcorrosion resistant alloys, such as stainless steel (e.g., grade-316 Lstainless steel), it should be appreciated that a bioreactor may be madeof glass, ceramics, plastic, or any number of materials or combinationsthereof.

The term “manufacturing-scale bioreactor culture” refers to a bioreactorculture of scale sufficient to produce commercial or production scalequantities of a molecule (e.g., a therapeutic protein, e.g., atherapeutic antibody). In some embodiments, a manufacturing-scalebioreactor culture has a working volume (e.g., of culture medium) of atleast 500 L, at least 1000 L, at least 2000 L, at least 3000 L, at least4000 L, at least 5000 L, at least 7500 L, at least 10000 L, at least12500 L, at least 15000 L, at least 20000 L, at least 100000 L, or more.In some embodiments, the manufacturing-scale bioreactor culture has aworking volume of 2000 L or 15000 L. In some embodiments, amanufacturing-scale bioreactor culture has a working volume in a rangeof 500 L to 1000 L, 500 L to 2500 L, 500 L to 5000 L, 500 L to 10000 L,500 L to 15000 L, 500 L to 20000 L, 500 L to 100000 L, 2000 L to 5000 L,2000 L to 10000 L, 2000 L to 15000 L, 2000 L to 20000 L, 2000 L to100000 L, 15000 L to 20000 L, 15000 L to 100000 L, 20000 L to 50000 L,20000 L to 100000 L, or 50000 L to 100000 L. In some embodiments, amanufacturing-scale bioreactor culture produces or is capable ofproducing at least 1 gram, at least 10 grams, at least 100 grams, 500grams, 1000 grams, 2000 grams, 3000 grams, or more of a molecule. Insome embodiments, a manufacturing-scale bioreactor culture produces oris capable of producing 1 gram to 10 grams, 1 gram to 100 grams, 1 gramto 500 grams, 10 gram to 1000 grams, 10 grams to 2000 grams, 100 gramsto 1000 grams, 500 grams to 5000 grams, or more of a molecule. Forexample, a manufacturing-scale bioreactor may be a custom ABECmanufacturing-scale stainless steel bioreactor (e.g., in range of 2000 Lto 15000 L).

The term “test bioreactor culture” refers to a bioreactor culture of ascale smaller than a manufacturing-scale bioreactor culture butsufficient for research and development purposes, e.g., to establishappropriate cell growth conditions, protein purification methods, etc.In some embodiments, a test-scale bioreactor culture produces less than1 gram of a therapeutic molecule. In some embodiments, a test-scalebioreactor culture is a bench-scale bioreactor culture that has aworking volume of 20 L or less, 10 L or less, 5 L or less, 4 L or less,3 L or less, 2 L or less, 1 L or less, or 0.1 L or less. In someembodiments, the bench-scale bioreactor culture has a working volume ofabout 3 L. In some embodiments, a test-scale bioreactor culture is abench-scale bioreactor culture that has a working volume of in a rangeof 100 mL to 500 mL, 100 mL to 1 L, 100 mL to 2 L, 100 mL to 5 L, 100 mLto 10 L, 100 mL to 20 L, 1 L to 3 L, 1 L to 5 L, 1 L to 10 L, or 1 L to20 L. For example, the bench-scale bioreactor may be a 3 L or 5 LApplikon (Applikon Biotechnology, The Netherlands) bench-scale glassbioreactor. In some embodiments, a test-scale bioreactor culture is of apilot-scale that has a working volume from 20 L to 1000 L or 50 L to1000 L. In some embodiments, a test-scale bioreactor culture is apilot-scale bioreactor culture that has a working volume in a range of20 L to 100 L, 20 L to 250 L, 20 L to 500 L, 20 L to 1000 L, 100 L to250 L, 100 L to 500 L, 100 L to 1000 L, 250 L to 500 L, 250 L to 1000 Lor 500 L 1000 L. In some embodiments, the pilot-scale bioreactor culturehas a working volume of 200 L. For example, the pilot-scale bioreactormay be a 200 L custom ABEC (ABEC Inc., Bethlehem, Pa.) pilot-scalestainless steel bioreactor.

It should be appreciated that the bioreactor scale may be defined byrelative working volume capacity. In some embodiments, the test-scalebioreactor has is in a range of 0.0005% to 90%, 0.005% to 90%, 0.05% to90%, 0.5% to 90%, 0.0005% to 50%, 0.005% to 50%, 0.05% to 50%, 0.5% to50%, 0.0005% to 25%, 0.005% to 25%, 0.05% to 25%, or 0.5% to 25% of theworking volume of the manufacturing-scale bioreactor. In someembodiments, the bench-scale bioreactor has between 0.0005% and 4% theworking volume of the manufacturing-scale bioreactor. In someembodiments, the pilot-scale bioreactor has between 0.1% and 90% theworking volume of the manufacturing-scale bioreactor. In someembodiments, the bench-scale bioreactor has about 0.15% the workingvolume of the manufacturing-scale bioreactor. In some embodiments thepilot-scale bioreactor has about 10% the working volume of themanufacturing-scale bioreactor.

Methods provided herein utilize models (e.g., statistical models) thatrelate Raman spectral data obtained from a bioreactor culture to aculture parameter. The term “culture parameter” refers to anyphysicochemical or cellular characteristic of the culture including, forexample, the level of a constituents, the tonicity of a culture, theosmolality of a culture, the pH of a culture, the level of a cell (e.g.,mammalian cell, insect cells, yeast cell, bacterial cells) in a culture,and other similar parameters.

It should be appreciated that a culture parameter may refer to anycomponent of a culture, including but not limited to serum components,nutrient components, waste components, biological cells or biologicalproducts. In some embodiments, a culture parameter being assessed may bea molecular parameter, a cellular parameter, or a chemical parameter. Insome embodiments a culture component is a nutrient, protein, peptide,carbohydrate, growth factor, cytokine or salt. For example, a culturecomponent may be glucose glutamine, glutamate, lactose, ammonium oranother component. In some embodiments, a culture component is a proteinor other molecule, including a therapeutic or clinical molecule,expressed in or by a cell of a culture. In some embodiments, a proteinor other molecule of interest is expressed in or by a recombinant cell.In some embodiments, a cell of a culture is a bacterial cell, a yeastcell, a plant cell, a mammalian cell, an insect cell, an algal cell oranother cell type.

In some embodiments, a culture parameter may be a cellular parameter. Itshould be appreciated that a cellular parameter may pertain to any cellor collection of cells of the bioreactor culture. In some embodiments, acell may be a bacterial cell, a yeast cell, a plant cell, a mammaliancell, an insect cell, an algal cell or another cell type. In someembodiments, a cellular parameter is viable cell density (VCD). In someembodiments, a cellular parameter is total cell density (TCD). In someembodiments, a bioreactor contains more than one cell type. In someembodiments, a cellular parameter is the VCD or TCD of one or more celltypes when more than one cell types are present in the bioreactorculture.

In some embodiments, a culture parameter is a chemical parameter. Itshould be appreciated that a chemical parameter may pertain to anychemical species present in a bioreactor culture. In some embodiments, achemical parameter is the level of a molecule. In some embodiments, achemical parameter is a level of a dissolved gas. In some embodiments, achemical parameter is pH. In some embodiments, a chemical parameter is alevel of concentration of a salt or ion.

Multivariate Models

Aspects of the disclosure relate to methods of assessing a bioreactorculture using Raman spectroscopy. In some embodiments, a cultureparameter of a bioreactor culture is modeled in a test-scale bioreactorusing Raman spectroscopy, in which the model is used to predict amolecular parameter in a manufacturing-scale bioreactor culture based ona Raman spectra of the manufacturing-scale bioreactor culture. In someembodiments, the method of assessing a culture parameter of amanufacturing-scale bioreactor culture comprises obtaining a Ramanspectrum of a manufacturing-scale bioreactor culture and determining aculture parameter of the manufacturing-scale bioreactor culture using amodel that relates the Raman spectrum to the molecular parameter, inwhich the model is developed using at least one test-scale bioreactorculture.

Any appropriate statistical model may be used in methods disclosedherein. In some embodiments, the model is a regression model thatrelates predicted variables (e.g., culture parameters) and observablevariables (e.g., Raman spectral data). In some embodiments, theregression model is a partial least squares model. In some embodiments,the model is a bilinear factor model that projects predicted variables(e.g., culture parameters) and observable variables (Raman spectraldata) into a new space. In some embodiments, the model is a regressionmodel that uses principal components analysis (PCA) for estimatingunknown regression coefficients in the model. However, othermultivariate analytical techniques may be used including, for example,support vector machines, multivariate linear regression, and others.

In some embodiments, Raman spectroscopy data is analyzed usingmultivariate partial least square (PLS) models. As disclosed herein, aset of single-scale and combination-scale models have been generated forvarious nutrient, metabolic waste and cell growth parameters of a cellculture process.

In some embodiments, methods provided herein involve multivariate modelsbased on Raman spectral data obtained across multiple different scalesof bioreactor cultures, including, for example, bench (e.g., up to 20 L)and/or pilot-scales (e.g., between 20 L and 1000 L), that are accurateat across a range of scales, including manufacturing-scales (e.g., 500 Lor more). In some embodiments, by gathering data at bench, pilot andmanufacturing-scales, models incorporate tremendous process variability,offering a great deal of robustness to the final model.

It should be appreciated that univariate-Y, multivariate-X predictivecomponent models may be built from spectral data collected frombioreactors (e.g., during fed-batch cell culture processing). In someembodiments, no spectral data is collected off line or via referencesolutions of chemically defined media or nutrient solutions. In oneaspect, spectra are exported from an Raman instrument, e.g., a Ramanprobe configured for real-time measurements of Raman spectral data. Inone embodiment, mathematical pre-processing methods are applied to allX-block cell culture spectra (e.g., including smoothing (e.g., 1stderivative Savitzky-Golay smoothing with 15 cm-1 point spacing and aquadratic polynomial) followed by Standard Normal Variate (SNV) andmean-centering). In one embodiment, Y-block reference component data istreated with unit-variance scaling (UV). In one embodiment, Y-blockreference data outliers are identified and removed through analysis ofscores plots, loading plots, residual plots, and Hotelling's T² plots orother appropriate techniques. The variable influence on projection (VIP)algorithm, which ranks the importance of the X-variables (spectralregions) taking into consideration the amount of explained variation inthe Y-component variance (offline measurements), may be calculated andused to determine relative correlation of spectral regions throughoutthe full Raman shift range with offline data.

In some embodiments, because Raman spectroscopy is a form of vibrationalspectroscopy, a user may have more confidence in identifying strongcorrelations within the Raman spectrum via modeling for chemicallydiscrete components such as glucose, lactate, and others. Cellulardensity values are much less chemically discrete and may be based onimaging software techniques coupled with microscopes or visually with ahemocytometer. However, it has been found that robust Raman wavenumberregions and data preprocessing steps for generating strong correlationswith VCD and TCD without experiencing the scale-dependent predictionquality may be used.

Many organic compounds naturally fluoresce and fluorescence interferencebackground signal may change (e.g., increase) throughout a batch due tocontinuous accumulation of cells or cellular metabolism componentswithin the system. Fluorescence interference may display a strongersignal than the inelastic Raman scattering which may lead to largebackgrounds in Raman spectra as well as a degradation of the signal tonoise ratio of the inelastic scattering data of interest. In thiscontext, the use of longer wavelength excitation sources, such as 1064nm, yield fluorescence responses in fewer materials. A five-foldimprovement in glucose measurement capability, due to reducedfluorescence interference within a complex aqueous sample environment,was obtained when switching from 514.5 nm to 785 nm laser wavelengthexcitation.

Fluorescent background can also be managed by employing preprocessingand baseline normalization techniques to Raman spectral data, includingfirst and second differentiation,

Savitzky-Golay smoothing differentiation, SNV, multiplicative signalcorrection (MSC), extended multiplicative signal correction (EMSC)polynomial fitting, Fourier Transform, wavelet analysis, orthogonalsignal correction (OSC) and extended inverted signal correction (EISC)among others. In some embodiments, the use of normalization techniquesare not applicable to a long-duration fed-batch cell culture processesas the level of fluorescence interference is gradually scaled into thedata over time meaning efficient normalization must be dynamic to alarge group of spectra rather than static to a constant fluorescentbackground. In some embodiments, a consideration surrounding the choiceof laser wavelength excitation involves the sample degradation potentialof shorter wavelength, higher frequency lasers as photochemicaldegradation of biological samples may occur at 514.5 nm but not with 660nm excitation wavelength.

In some embodiments, a universal cross-program models is provided thatmanages (e.g., through normalization) differences in peak cell growthand metabolic rates across different cell culture processes. In suchembodiments, the Raman multivariate PLS models are built from onemammalian cell culture process and applied to another.

Further aspects of the disclosure relate to multivariate analyses usingof Raman spectroscopy to monitor or assess bioreactor culture conditionsin real-time. For example, multi-component, multi-scale Ramanspectroscopy modeling may be used to monitor in real-time a bioreactorculture comprising cells engineered to produce a therapeutic protein(e.g., a monoclonal antibody). Culture conditions can be altered inresponse to the real-time information to maintain culture componentswithin acceptable limits for production of the therapeutic protein.

In some embodiments, multivariate analysis techniques are disclosedherein that are advantageous because they do not involveoverly-iterative processes. In particular, simplified protocols areprovided for addressing relevant analytical steps including spectralpreprocessing, spectral region selection, and outlier removal to createmodels exclusively from cell culture process data without the inclusionof spectral data from chemically defined nutrient solutions or targetedcomponent spiking studies. Thus, an array of single-scale andcombination-scale modeling iterations may be generated to evaluatetechnology capabilities and model scalability. Using such methods,analysis of prediction errors across models has shown that glucose,lactate, osmolality, glutamate and ammonia are well modeled, forexample. Model strength may be assessed via predictive validation and byexamining performance similarity across single-scale andcombination-scale models. Accurate predictive models were generated forviable cell density (VCD) and total cell density (TCD), but demonstratedscale-dependencies in cross-scale predictions where only developmentdata was used in calibration. Thus, for certain parameters,scale-dependencies may be considered when generating accurate predictivemodels. Glutamate and ammonia models also demonstrated accuratepredictions within and across scales.

Aspects of the disclosure relate to the generation of biological samplegrowth and metabolite predictive models across development andmanufacturing-scale bioreactors using in situ Raman spectroscopy. Ramanspectroscopy provides a viable option for real-time bioreactormonitoring due to its ability to measure a myriad of chemical specieswith minimal interference from water, thus enabling in situ, real timeprocess knowledge and quantification of a bioreactor environment.Research establishing chemical detection and measurement specificitywithin simple aqueous solutions is available as a reference forbiochemically relevant components including glucose and other sugarcarbohydrates, lactic acid and sodium lactate, glutamic acid and itsderivatives, as well as non-aqueous solutions of liquid-phase ammonia.Progress has extended from the demonstration of neat component solutionmeasurement specificity to complex aqueous solution applications relatedto the biomedical and biotechnology industries. Some examples of theseareas include: a) clinically precise quantitative prediction of bloodanalytes for glucose, urea, albumin and hemoglobin in samples stillcontaining red blood cells previously believed to distort and degradethe quality of optical data, establishing the limits of detection ofglucose, lactate, glutamine and ammonia for different laser wavelengthsin the spent media supernatant material of bioreactor samples, c)quality control motivated pre-screening of the chemically definedcomplex basal and feed media preparations used in mammalian cell culturewith the aim of establishing a robust lot acceptance criteria and d) thepre-batch estimation of cell culture process yield via calibration ofnon-aqueous raw material spectroscopic data and batch output attributes.

Raman Spectroscopy

Raman spectroscopy relies on the inelastic scattering observed when aphoton is impinged upon a chemical bond. The Raman equipment firesphotons of a specific wavelength (energy level) at a target to beanalyzed. When the photons enter the electron cloud of the chemical bondthey are converted into energy and then back into photons and ejectedfrom the bond. With inelastic scattering, the photon loses energy in theform of a wavelength shift. This wavelength shift is measured by theRaman system and the frequency of occurrence for all shifts is added togenerate peaks (resulting in a Raman spectrum). These peaks, whichrepresent a count of Raman shifts at a given energy, can be correlatedto specific constituents in the system. In some embodiments, theintensities of one or more peaks can be used to determine theconcentration of a component in a solution (e.g., by comparing to astandard curve of intensities generated using known concentrations).

In some embodiments, the Raman spectroscopy may be performed in thevisible, near infrared, infrared, near ultraviolet, or ultraviolet (UV)range. In some embodiments, a signal enhancement technique known asSurface Enhanced Raman Spectroscopy (SERS), which relies on a phenomenonknown as surface plasmonic resonance, may be used. In some embodiments,resonance Raman spectroscopy, tip-enhanced Raman spectroscopy, polarizedRaman spectroscopy, stimulated Raman spectroscopy, transmission Ramanspectroscopy, spatially offset Raman spectroscopy, difference Ramanspectroscopy, Fourier Transform (FT) Raman, or hyper Raman spectroscopymay be used. In some embodiments, a Raman analyzer can be used that isconfigured with a laser or other suitable light source that operates atappropriate wavelengths (e.g., 325 nm, 514.5 nm, 532 nm, 632.8 nm, 647nm, 752 nm, 785 nm, 830 nm, 1064 nm, etc.)

In some embodiments, data fusion may be used to augment thespectroscopic analysis. For example, a second spectroscopic analysis(e.g., Nuclear Magnetic Resonance (NMR), X-Ray Fluorescence (XRF), SmallAngle X-Ray Scattering (SAXS), Powder Diffraction, Near InfraredSpectroscopy (NIR), or Fourier Transform Infrared Spectroscopy (FTIR))may be performed to obtain a second spectrum of a lot sample, and datafusion analysis may be used to evaluate the lot sample.

In some embodiments, prediction models that are to be used herein forevaluating culture components are developed using a training data setbased on one or more informative subsets or an entire Raman spectra,e.g., across 500 cm⁻¹ to 1700cm⁻¹. For example, in some embodiments, aprediction model (e.g., a PLS model) will be established based on usefultraining information present within the entire spectra across 500cm⁻¹-1700cm⁻¹.

In one aspect, the disclosure provides methods of defining a Ramansignature of a culture component and using the Raman signature toestablish the level of the culture component within the bioreactorculture. In some embodiments, methods comprise obtaining a Ramanspectrum of a culture component in a non-interfering orminimally-interfering solution, identifying peaks in the Raman spectrumthat are associated with the culture component, obtaining a Ramanspectrum of a culture medium comprising the culture component, and,removing peaks of the culture component in the Raman spectrum of theculture medium that are distorted compared to the peaks identified inthe Raman spectrum of the culture component in a non-interfering orminimally-interfering solution. In some embodiments, the distorted peaksare laterally shifted peaks and inverted peaks. In some embodiments, thelaterally shifted peak or inverted peak is removed if it is shifted bymore than 5 cm^(−l)in a concentration dependent fashion. In someembodiments of methods provided herein, the culture component isglucose. In some embodiments of methods provided herein, the culturecomponent is lactate, glutamate, ammonia or osmolality. In someembodiments of methods provided herein, the culture component is VCD orTCD.

In one aspect of methods provided herein, a Raman spectrum of acomponent in a non-interfering or minimally-interfering solution isobtained. A non-interfering or minimally-interfering solution is asolution that allows for the generation of a Raman spectrum of acomponent with little to no interference of the component with otheragents in the solution. In some embodiments, a non-interfering orminimally-interfering solution would be water, which may or may not haveadditional non-interfering or minimally-interfering components, such asbuffers or salts. However, other non-interfering orminimally-interfering solutions may be used as aspects of the disclosureare not limited in this respect.

In some embodiments, a Raman spectra may be obtained of a component ofinterest (e.g., glucose) dissolved in a simple solvent such as water(e.g., by using an excitation laser) for purposes of determining thelocation of peaks or other components of a Raman spectrum that relate tothe component. In some embodiments, Raman spectra may be obtained ofmultiple samples of a particular culture component at multipleconcentrations. The samples used to build this spectral Raman librarycover a range of concentrations that represents a reasonableapproximation of the experimental range (e.g., the concentration rangeof the component in a culture medium). In some embodiments, a particularcomponent is at a concentration in a range of 0.001 g/L to 0.05 g/L,0.001 g/L to 0.1 g/L, 0.001 g/L to 0.5 g/L, 0.001 g/L to 1.0 g/L, 0.001g/L to 10 g/L, 0.01 g/L to 0.05 g/L, 0.01 g/L to 0.1 g/L, 0.01 g/L to0.5 g/L, 0.01 g/L to 1.0 g/L, 0.01 g/L to 10 g/L, 0.1 g/L to 0.5 g/L,0.1 g/L to 1.0 g/L, 0.1 g/L to 10 g/L, 0.5 g/L to 1.0 g/L, or 0.5 g/L to10 g/L. Thus, for instance, Raman spectra may be obtained from the samecomponent at different concentration increments, such as increments of0.001 g/L, 0.005 g/L, 0.01 g/L, 0.05 g/L, 0.1 g/L, 0.2 g/L, 0.3 g/L, 0.4g/L, 0.5 g/L, 0.6 g/L, 0.7 g/L, 0.8 g/L, 0.9 g/L, 1.0 g/L, etc. The dataobtained by using these Raman spectra are analyzed, includingderivatizing and normalizing of the data if needed. Computer programs,including statistical software, may be used in this process. The dataanalysis results in peaks in the Raman spectrum that represent the basispeaks for the component of interest. The spectra are correlated with theknown concentration of the component of interest (e.g., glucose).

In some embodiments, Raman spectra are also obtained of variousconcentrations of a component of interest (e.g., glucose) added to aculture medium of interest. It should be appreciated that the culturemedium of interest may have a variety of make ups. However, the culturemedium of interest ideally mimics closely the biological productionculture medium and should include the major components present in cellculture media (polypeptide, sugars, salts, nucleic acids, cellulardebris, and nutrients). The peaks identified in the Raman spectra of thecomponent of interest (e.g., glucose) in the non-interfering orminimally-interfering solution are used to identify peaks in the Ramanspectra of the component of interest (e.g., glucose) in the culturemedium. The spectra of the component of interest (e.g., glucose) in theculture medium are trimmed to match the previously the peaks identifiedin the Raman spectra of the component of interest (e.g., glucose) in thenon-interfering or minimally-interfering solution. In some embodiments,the spectra are trimmed by removing peaks that are distorted. In someembodiments, peaks that are distorted are peaks that are laterallyshifted or inverted. However, it should be appreciated that distortedpeaks may include any peak that fails to meet certain criteria (e.g.,intensity, signal-to-noise (S/N) ratio, shape, closeness to otherpeaks). Distorted peaks can be identified by visual inspection or byusing a computer program that identifies (and removes) peaks that do notmeet certain criteria. For example, peaks may be excluded because theyare laterally shifted or inverted by at least 5%, at least 10%, at least20%, at least 30%, at least 40%, or at least 50% compared with areference peak (e.g., a non-distorted peak). Similarly, peaks may beexcluded because they have a S/N ratio that is at least 5%, at least10%, at least 20%, at least 30%, at least 40%, or at least 50% less thanthe S/N of a reference peak (e.g., a non-distorted peak).

In some embodiments, only a portion of a Raman spectrum is evaluated.For example, data relating to only a portion of the Raman spectrum isevaluated and the remaining data is filtered or otherwise removed priorto analysis. In some embodiments, the distorted peaks that are removedare lateral peak shifts. In some embodiments, a lateral peak shift lookslike a 2-dimensional peak that has been stretched out. This peakdistortion is likely the result of a component in the culture mediumthat is interacting with one of the bonds on the molecule of interest,the presence of a bond with similar character, solvent distortion, orany combination of these phenomena. In some embodiments, the laterallyshifted peak or inverted peak is shifted by more than 5 cm⁻¹ in aconcentration dependent fashion. In some embodiments, the lateral peakis removed if it is shifted by more than 1 cm⁻¹, more than 2 cm⁻¹, morethan 5 cm⁻¹, more than 10 cm⁻¹, or more than 20 cm⁻¹ or more. In someembodiments, the lateral peak is removed if it is shifted by more than 1cm⁻¹, more than 2 cm⁻¹, more than 5 cm⁻¹, more than 10 cm⁻¹, or morethan 20 cm⁻¹ or more, in a concentration dependent fashion.

In some embodiments, the distorted peaks that are removed are inversionpeaks (also called “inverted peaks” herein). As provided for instance inthe figures herein, an inversion peak is a peak where it appears thatthe lower concentration data is higher in magnitude than the highconcentration data, when this relationship did not exist in the basispeaks. This type of distortion is usually due to a molecular specieswithin the media that has similar vibrational properties and thereforesimilar peaks. In some embodiments, the inverted peak is removed ifthere is a lack of baseline.

In one aspect, the spectra from which the distorted peaks have beenremoved provide the Raman signature of the culture component. However itshould be appreciated that the Raman signature may be further refined byusing the trimmed spectra with the identified peaks through a largercell culture dataset, e.g., by building a predictive PLS (partial leastsquare) model. For instance, relevant cell culture spectra could beincluded along with the corresponding offline data for the constituentof interest into a multivariate software package such as SIMCA or thePLS Toolbox add-on for Matlab. In some embodiments, offline constituentdata are collected through an appropriate analytical method and added tothe model.

In some embodiments, the Raman signature comprises a selected number ofpeaks and associated peak ranges that allow for the evaluation (e.g.,identification) of a culture component in a culture medium. In someembodiments, the Raman signature comprises a selected number of peaksand associated peak ranges that allow for the evaluation of the level ofa culture component in a culture medium. In some embodiments, a Ramansignature of a culture component comprises multiple combinations ofidentifying peaks. It should be appreciated that a minimal number ofpeaks may define a Raman signature. However, additional peaks may helprefine the Raman signature. Thus, for instance, a Raman signatureconsisting of 4 peaks may provide a 95% certainty that a culturecomposition that shows those peaks contains the component associatedwith the Raman signature. However, a Raman signature consisting of 10peaks may provide a 99% certainty that a culture composition that showsthose peaks contains the component associated with the Raman signature.Similarly, a Raman signature consisting of 4 peaks may provide a 90%certainty that a culture composition that shows those peaks contains thecomponent at the level of the component associated with the Ramansignature. However, a Raman signature consisting of 10 peaks may providea 98% certainty that a culture composition that shows those peakscontains the component at the level of the component associated with theRaman signature.

In one aspect, the disclosure provides Raman signatures of culturecomponents. In some embodiments, the culture component is glucose. Insome embodiments, the disclosure provides Raman signatures of glucosethat allow for evaluating the presence of glucose in a sample. In someembodiments, the disclosure provides Raman signatures of glucose thatallow for evaluating the level of glucose in a sample. In someembodiments, the disclosure provides Raman signatures of glucose thatallow for evaluating the presence of glucose in a culture medium. Insome embodiments, the disclosure provides Raman signatures of glucosethat allow for evaluating the level of glucose in a culture medium.

In one aspect, the Raman signature of glucose comprises peaks in the 200cm⁻¹ to 3400 cm⁻¹ wavenumber range. As used herein, wavenumber refers tothe spatial frequency of a wave, which may be in cycles per unitdistance or radians per unit distance. In some embodiments, the Ramansignature of glucose comprises at least 4 peaks in the 200 cm⁻¹ to 3400cm⁻¹ wavenumber range. In some embodiments, the Raman signature ofglucose comprises at least 6 peaks in the 200 cm⁻¹ to 3400 cm⁻¹wavenumber range. In some embodiments, the Raman signature of glucosecomprises at least 10 peaks in the 200 cm⁻¹ to 3400 cm⁻¹ wavenumberrange. In some embodiments, the Raman signature of glucose comprises atleast 20 peaks in the 200 cm⁻¹ to 3400 cm⁻¹ wavenumber range. In someembodiments, the Raman signature of glucose comprises at least 1, atleast 2, at least 3, at least 4, at least 5, at least 6, at least 7, atleast 8, at least 9, at least 10, at least 11, at least 12, at leastl3,at least 14, at least 15, at least 16, at least 17, at least 18, atleast 19, at least 20, at least 25, or at least 30 peaks in the 200 cm⁻¹to 3400 cm⁻¹ wavenumber range.

In some embodiments, the Raman signature of glucose comprises at least 4peaks in the 200 cm⁻¹ to 3400 cm⁻¹ wavenumber range. In someembodiments, the 4 peaks are selected from the following 6 peaks:

peak 1, range: 364-440, peak 402 (all in cm⁻¹),

peak 2, range: 511-543, peak 527 (all in cm⁻¹),

peak 3, range: 577-600, peak 589 (all in cm⁻¹),

peak 4, range: 880-940, peak 911 (all in cm⁻¹),

peak 5, range: 1130-1180, peak 1155 (all in cm⁻¹), and

peak 6, range: 1262-1290, peak 1276 (all in cm⁻¹).

In some embodiments, the set of 4 selected peaks is: {peak 1, peak 2,peak 3, peak 4}, {peak 1, peak 2, peak 3, peak 5}, {peak 1, peak 2, peak3, peak 6}, {peak 1, peak 2, peak 4, peak 5},{peak 1, peak 2, peak 4,peak 6}, {peak 1, peak 2, peak 5, peak 6}, {peak 1, peak 3, peak 4, peak5}, {peak 1, peak 3, peak 4, peak 6}, {peak 1, peak 3, peak 5, peak 6},{peak 1, peak 4, peak 5, peak 6}, {peak 2, peak 3, peak 4, peak 5},{peak 2, peak 3, peak 4, peak 6}, {peak 2, peak 3, peak 5, peak 6},{peak 2, peak 4, peak 5, peak 6}, or {peak 3, peak 4, peak 5, peak 6}.

In some embodiments, the Raman signature of glucose comprises at least 6peaks in the 200 cm⁻¹ to 3400 cm⁻¹ wavenumber range. In someembodiments, the 6 peaks are:

peak 1, range: 364-440, peak 402 (all in cm⁻¹),

peak 2, range: 511-543, peak 527 (all in cm⁻¹),

peak 3, range: 577-600, peak 589 (all in cm⁻¹),

peak 4, range: 880-940, peak 911 (all in cm⁻¹),

peak 5, range: 1130-1180, peak 1155 (all in cm⁻¹), and

peak 6, range: 1262-1290, peak 1276 (all in cm⁻¹).

In some embodiments, the Raman signature of glucose comprises at least10 peaks in the 200 cm⁻¹ to 3400 cm⁻¹ wavenumber range. In someembodiments, the 10 peaks are:

peak 1, range: 364-440, peak 402 (all in cm⁻¹),

peak 2, range: 511-543, peak 527 (all in cm⁻¹),

peak 3, range: 577-600, peak 589 (all in cm⁻¹),

peak 4, range: 749-569, peak 759 (all in cm⁻¹),

peak 5, range: 880-940, peak 911 (all in cm⁻¹),

peak 6, range: 1050-1070, peak 1060 (all in cm⁻¹),

peak 7, range: 1110-1140, peak 1125 (all in cm⁻¹),

peak 8, range: 1130-1180, peak 1155 (all in cm⁻¹),

peak 9, range: 1262-1290, peak 1276 (all in cm⁻¹), and

peak 10, range: 1520-1578, peak 1549 (all in cm⁻¹).

In some embodiments, the Raman signature of glucose comprises at least20 peaks in the 200 cm⁻¹ to 3400 cm⁻¹ wavenumber range. In someembodiments, the 20 peaks are:

peak 1, range: 364-440, peak 402 (all in cm⁻¹),

peak 2, range: 511-543, peak 527 (all in cm⁻¹),

peak 3, range: 577-600, peak 589 (all in cm⁻¹),

peak 4, range: 720-740, peak 732 (all in cm⁻¹),

peak 5, range: 769-799, peak 789 (all in cm⁻¹),

peak 6, range: 835-875, peak 855 (all in cm⁻¹),

peak 7, range: 880-940, peak 911 (all in cm⁻¹),

peak 8, range: 950-1015, peak 968 (all in cm⁻¹),

peak 9, range: 1050-1070, peak 1060 (all in cm⁻¹),

peak 10, range: 1063-1080, peak 1073 (all in cm⁻¹),

peak 11, range: 1110-1140, peak 1125 (all in cm⁻¹),

peak 12, range: 1130-1180, peak 1155 (all in cm⁻¹),

peak 13, range: 1190-1240, peak 1210 (all in cm⁻¹),

peak 14, range: 1262-1290, peak 1276 (all in cm⁻¹),

peak 15, range: 1330-1342, peak 1336 (all in cm⁻¹),

peak 16, range: 1350-1380, peak 1371 (all in cm⁻¹),

peak 17, range: 1390-1410, peak 1401 (all in cm⁻¹),

peak 18, range: 1425-1475, peak 1450 (all in cm⁻¹),

peak 19, range: 1465-1480, peak 1473 (all in cm⁻¹), and

peak 20, range: 1520-1578, peak 1549 (all in cm⁻¹).

In some embodiments, a culture component is lactate. In someembodiments, the disclosure provides Raman signatures of lactate thatallow for evaluating the presence of lactate in a sample. In someembodiments, the disclosure provides Raman signatures of lactate thatallow for evaluating the level of lactate in a sample. In someembodiments, the disclosure provides Raman signatures of lactate thatallow for evaluating the presence of lactate in a culture medium. Insome embodiments, the disclosure provides Raman signatures of lactatethat allow for evaluating the level of lactate in a culture medium.

In one aspect, the Raman signature of lactate comprises peaks in the 200cm-1 to 3400 cm-1 wavenumber range (FIGS. 6A-D). In some embodiments,the Raman signature of lactate comprises at least 2 peaks in the 200cm-1 to 3400 cm-1 wavenumber range. In some embodiments, the Ramansignature of lactate comprises at least 4 peaks in the 200 cm-1 to 3400cm-1 wavenumber range. In some embodiments, the Raman signature oflactate comprises at least 6 peaks in the 200 cm-1 to 3400 cm-1wavenumber range. In some embodiments, the Raman signature of lactatecomprises at least 7 peaks in the 200 cm-1 to 3400 cm-1 wavenumberrange. In some embodiments, the Raman signature of lactate comprises atleast 1, at least 2, at least 3, at least 4, at least 5, at least 6 orat least 7 peaks in the 200 cm-1 to 3400 cm-1 wavenumber range.

In some embodiments, the Raman signature of lactate comprises at least 2peaks in the 200 cm-1 to 3400 cm-1 wavenumber range. In someembodiments, the 2 peaks are selected from the following 7 peak ranges:

peak 1, range: 500-575, (all in cm-1),

peak 2, range: 820-880, (all in cm-1),

peak 3, range: 900-950, (all in cm-1),

peak 4, range: 1010-1110, (all in cm-1),

peak 5, range: 1290-1340, (all in cm-1),

peak 6, range: 1360-1510, (all in cm-1), and

peak 7, range: 2840-3020, (all in cm-1).

In some embodiments, the set of 2 selected peaks is: {peak 1, peak 2},{peak 1, peak 3 }, {peak 1, peak 4}, {peak 1, peak 5},{peak 1, peak 6},{peak 1, peak 7}, {peak 2, peak 3 }, {peak 2, peak 4}, {peak 2, peak 5},{peak 2, peak 6}, {peak 2, peak 7}, {peak 3, peak 4}, {peak 3, peak 5},{peak 3, peak 6}, {peak 3, peak 7}, {peak 4, peak 5}, {peak 4, peak 6},{peak 4, peak 7}, {peak 5, peak 6}, {peak 5, peak 7}, or {peak 6, peak7}.

In some embodiments, the Raman signature of lactate comprises at least 4peaks in the 200 cm-1 to 3400 cm-1 wavenumber range. In someembodiments, the 4 peaks are selected from the following 7 peak ranges:

peak 1, range: 500-575, (all in cm-1),

peak 2, range: 820-880, (all in cm-1),

peak 3, range: 900-950, (all in cm-1),

peak 4, range: 1010-1110, (all in cm-1),

peak 5, range: 1290-1340, (all in cm-1),

peak 6, range: 1360-1510, (all in cm-1), and

peak 7, range: 2840-3020, (all in cm-1).

In some embodiments, the Raman signature of lactate comprises at least 6peaks in the 200 cm-1 to 3400 cm-1 wavenumber range. In someembodiments, the 6 peaks are selected from the following 7 peak ranges:

peak 1, range: 500-575, (all in cm-1),

peak 2, range: 820-880, (all in cm-1),

peak 3, range: 900-950, (all in cm-1),

peak 4, range: 1010-1110, (all in cm-1),

peak 5, range: 1290-1340, (all in cm-1),

peak 6, range: 1360-1510, (all in cm-1), and

peak 7, range: 2840-3020, (all in cm-1).

In some embodiments, the Raman signature of lactate comprises at least 7peaks in the 200 cm-1 to 3400 cm-1 wavenumber range. In someembodiments, the 7 peaks are:

peak 1, range: 500-575, (all in cm-1),

peak 2, range: 820-880, (all in cm-1),

peak 3, range: 900-950, (all in cm-1),

peak 4, range: 1010-1110, (all in cm-1),

peak 5, range: 1290-1340, (all in cm-1),

peak 6, range: 1360-1510, (all in cm-1), and

peak 7, range: 2840-3020, (all in cm-1).

It should be appreciated that, as for glucose and lactate, theparameters including but not limited to glutamate, ammonium, osmolality,VCD and TCD may be similarly evaluated.

Monitoring and Control of Culture Parameters

In one aspect, the disclosure provides methods for evaluating the levelof a component in of a bioreactor culture. As used herein, the term“level” refers to an amount or concentration of a molecule entity,chemical species, component, or object. In one aspect, the disclosureprovides methods for adjusting a level of a component in of a bioreactorculture. In some embodiments, the culture component is glucose.

The production of biological products, such as therapeutic proteins, bybiological processes has been used for many years. However, controllingthe composition of the culture medium remains challenging especially forlarge scale and continuous production methods. A problem of sub-optimaltiter has been described by the Crabtree effect and higher levelanalogs, e.g., a situation in which cell culture cells are exposed tohigh levels of glucose and oxidative phosphorylation is inhibited andcellular processes become stunted due to the stressful nature of theenvironment (Thomson J M, Gaucher E A, Burgan M F, De Kee D W, Li T,Aris J P, Benner S A. (2005). “Resurrecting ancestral alcoholdehydrogenases from yeast.” Nat Genet. 37 (6): 630-635). The Crabtreeeffect (as well as mammalian analogs) is seen as an acceptable tradeofffor the nutrient-deficiency safety buffer offered by high levels ofglucose in a bolus feed strategy. A second issue is one of highlyvariable nutrient levels leading to erratic protein glycosylation,sialylation as well as other key product quality attributes. The effectsof inconsistent cell culture environments on product quality attributeshave been described previously (Castilho, Leda dos Reis. Animal celltechnology: from biopharmaceuticals to gene therapy. 2008. Taylor andFrancis Group. P138; Peifeng Chen, Sarah W. Harcum, Effects of elevatedammonium on glycosylation gene expression in CHO cells, MetabolicEngineering, Volume 8, Issue 2, March 2006, Pages 123-132).

A feed strategy of daily bolus feeds (fed-batch) provides a conservativebut sub-optimal approach to cellular productivity and product quality.The drawback to this conservative approach is two-fold. First, to ensurethat the culture is not depleted of nutrients in between data points,certain nutrients are kept at safe, high levels. These variable levels,of glucose for example, may limit the batch productivity andconsistency. By controlling these nutrient levels at a consistentconcentration throughout the run, the individual cell ultimatelyreceives a stable level of feed/nutrients. The second drawback is that asingle daily feed is designed to have all of the nutrients that theculture needs to sustain it until the next feed. Because this is fedinto the system over a relatively short period of time (less than 1hour) it causes substantial swings in the nutrient levels that the cellsare exposed to. This leads to inconsistencies in the product qualityoutput of the cells. Another challenge of current methods is thesampling requirement to determine the levels of components (e.g.,nutrients, metabolites). Sampling is a main source for labor andcontamination.

In one aspect, the disclosure provides methods for evaluating andadjusting a culture component level in a culture medium that addresseschallenges associated with current methods of biological production.

In one aspect, the disclosure provides methods of evaluating a culturecomponent level in a culture medium. In some embodiments, methodsprovided herein comprise obtaining a Raman spectrum of a culture medium,parsing the Raman spectrum with a Raman signature of the culturecomponent to identify peaks corresponding the culture component, andmeasuring the intensity of the identified peaks to evaluate the culturecomponent level in the medium. In some embodiments, methods providedherein further comprises adjusting the culture component level if thelevel is outside a predetermined range. In some embodiments, the culturecomponent is glucose.

An element of methods provided herein is obtaining a Raman spectrum ofthe culture medium and parsing the Raman spectrum with the Ramansignature of a culture component of interest (e.g., glucose). In someembodiments, a wide range Raman spectrum is obtained from the culturemedium (e.g., including all or many of the wavelengths that areassociated with Raman spectroscopy of components of culture mediums).However, in some embodiments, only narrow regions of the Raman spectrumthat correspond to the Raman signature of the component of interest areobtained and/or interrogated. In some embodiments, multiple Ramanspectra are obtained from different locations within a culture medium.The data from such multiple spectra may be averaged if appropriate.

In addition, in some embodiments, the intensity of a Raman spectra willbe evaluated to determine the level of the culture component in asample. In some embodiments, the intensity of a Raman spectra isevaluated only within one or more peaks of the spectra that correspondto a Raman signature to determine the level of the culture component ina sample. Thus, in some embodiments, only signature peaks need to beevaluated, and the intensity of an entire Raman spectra does not need tobe evaluated Alternatively, or in addition, the Raman spectra may beparsed with Raman signatures associated with a specific level of theculture component of interest in culture medium. In some embodiments,the culture component is glucose and the Raman spectrum of the culturemedium is parsed with the Raman signature of glucose in culture medium.In some embodiments, the intensity of the Raman spectra will beevaluated to determine the level of glucose in the sample.Alternatively, or in addition, the Raman spectra may be parsed withRaman signatures associated with a specific level of glucose in culturemedium. In some embodiments, the Raman spectrum is parsed with one ormore of the following Raman signatures:

1) a Raman signature of glucose comprising at least 4 peaks in the 200cm⁻¹ to 3400 cm⁻¹ wavenumber range. In some embodiments, the 4 peaks areselected from the following 6 peaks:

peak 1, range: 364-440, peak 402 (all in cm⁻¹),

peak 2, range: 511-543, peak 527 (all in cm⁻¹),

peak 3, range: 577-600, peak 589 (all in cm⁻¹),

peak 4, range: 880-940, peak 911 (all in cm⁻¹),

peak 5, range: 1130-1180, peak 1155 (all in cm⁻¹), and

peak 6, range: 1262-1290, peak 1276 (all in cm⁻¹).

2) a Raman signature of glucose comprising at least 6 peaks in the 200cm⁻¹ to 3400 cm⁻¹ wavenumber range. In some embodiments, the 6 peaksare:

peak 1, range: 364-440, peak 402 (all in cm⁻¹),

peak 2, range: 511-543, peak 527 (all in cm⁻¹),

peak 3, range: 577-600, peak 589 (all in cm⁻¹),

peak 4, range: 880-940, peak 911 (all in cm⁻¹),

peak 5, range: 1130-1180, peak 1155 (all in cm⁻¹), and

peak 6, range: 1262-1290, peak 1276 (all in cm⁻¹).

3) a Raman signature of glucose comprising at least 10 peaks in the 200cm⁻¹ to 3400 cm⁻¹ wavenumber range. In some embodiments, the 10 peaksare:

peak 1, range: 364-440, peak 402 (all in cm⁻¹),

peak 2, range: 511-543, peak 527 (all in cm⁻¹),

peak 3, range: 577-600, peak 589 (all in cm⁻¹),

peak 4, range: 749-569, peak 759 (all in cm⁻¹),

peak 5, range: 880-940, peak 911 (all in cm⁻¹),

peak 6, range: 1050-1070, peak 1060 (all in cm⁻¹),

peak 7, range: 1110-1140, peak 1125 (all in cm⁻¹),

peak 8, range: 1130-1180, peak 1155 (all in cm⁻¹),

peak 9, range: 1262-1290, peak 1276 (all in cm⁻¹), and

peak 10, range: 1520-1578, peak 1549 (all in cm⁻¹).

4) a Raman signature of glucose comprising at least 20 peaks in the 200cm⁻¹ to 3400 cm⁻¹ wavenumber range. In some embodiments, the 20 peaksare:

peak 1, range: 364-440, peak 402 (all in cm⁻¹),

peak 2, range: 511-543, peak 527 (all in cm⁻¹),

peak 3, range: 577-600, peak 589 (all in cm⁻¹),

peak 4, range: 720-740, peak 732 (all in cm⁻¹),

peak 5, range: 769-799, peak 789 (all in cm⁻¹),

peak 6, range: 835-875, peak 855 (all in cm⁻¹),

peak 7, range: 880-940, peak 911 (all in cm⁻¹),

peak 8, range: 950-1015, peak 968 (all in cm⁻¹),

peak 9, range: 1050-1070, peak 1060 (all in cm⁻¹),

peak 10, range: 1063-1080, peak 1073 (all in cm⁻¹),

peak 11, range: 1110-1140, peak 1125 (all in cm⁻¹),

peak 12, range: 1130-1180, peak 1155 (all in cm⁻¹),

peak 13, range: 1190-1240, peak 1210 (all in cm⁻¹),

peak 14, range: 1262-1290, peak 1276 (all in cm⁻¹),

peak 15, range: 1330-1342, peak 1336 (all in cm⁻¹),

peak 16, range: 1350-1380, peak 1371 (all in cm⁻¹),

peak 17, range: 1390-1410, peak 1401 (all in cm⁻¹),

peak 18, range: 1425-1475, peak 1450 (all in cm⁻¹),

peak 19, range: 1465-1480, peak 1473 (all in cm⁻¹), and

peak 20, range: 1520-1578, peak 1549 (all in cm⁻¹).

In one aspect, methods of evaluating a culture component level in aculture medium comprise adjusting the glucose level if the level isoutside a predetermined range. In some embodiments, methods furthercomprise adjusting the glucose level if the level is outside a range of0.5-12 g/L, 1-3 g/L, 1-5 g/L, or 1-10 g/L. In some embodiments, theglucose level may be increased if the level is below a reference orthreshold level, such as below 0.01 g/L, 0.1 g/L, 0.5 g/L, 1 g/L, 2.5g/L, or 5 g/L. In some embodiments, the glucose level may be decreasedif the level is above a reference or threshold level, such as above 3g/L, 5 g/L, 10 g/L or 12 g/L. It should be appreciated that the optimalglucose level will depend on the nature of the production method, thetype of cells, the duration of the production, the size of theproduction vessel, etc. However, a person of ordinary skill in the artcan determine an optimal or desired range of glucose for a particularconfiguration. Determining that a level of glucose is outside this range(e.g., if glucose levels are too low) can be used as a trigger to adjustthe glucose concentration. In some embodiments, methods further compriseadjusting the glucose level if the level is outside a range of 0.1-100g/L, 0.2-50 g/L, 0.5-10 g/L, 1-5 g/L, or 1-3 g/L. In some embodiments,methods further comprise adjusting the glucose level if the level ofglucose falls below 1 g/L. In some embodiments, methods further compriseadjusting the glucose level if the level of glucose falls below 3 g/L, 2g/L, 1 g/L, 0.5 g/L, or lower. It should be appreciated that peaksignatures and intensities that are used as references can beestablished for different concentrations of a compound of interest(e.g., glucose) and then used to determine the concentration of thecompound of interest.

In one aspect, methods of evaluating a culture component level in aculture medium further comprise evaluating an additional cultureparameter. In some embodiments, an additional culture parameter is oneor more of the following culture parameters: viable cell density, levelof lactate, level of glutamine, level of glutamate, level of ammonium,osmolality, or pH. It should be appreciated that the additionalparameters may be determined by any method, for instance, pH may bedetermined by a pH meter or a coloring agent, while viable cell densitymay be determined by non-Raman spectroscopic methods. In someembodiments, the one or more culture parameters are determined by Ramanspectroscopy. In some embodiments, evaluating the level of glucose andthe one or more culture parameters is done simultaneously.

In one aspect, methods of evaluating a culture component level in aculture medium are provided that comprise adjusting the glucose level ifthe level is outside a predetermined combination of ranges of glucoselevel and the one or more culture parameters. In some embodiments, theglucose level is adjusted if the level is outside a range of 1-3 g/L. Insome embodiments, methods of evaluating a culture component level in aculture medium comprise adjusting the one or more culture parameters ifthe one or more culture parameters are outside a predeterminedcombination of ranges of glucose level and the one or more cultureparameters. It should be appreciated that methods provided herein allowfor evaluation of and, the subsequent adjustment of, the level ofglucose and additional parameters if such levels fall outside apredetermined range. The ranges of glucose and the one or moreadditional parameters can be set independently, or in combination. Forinstance, a level of glucose of 1-2 g/L may be desired if the viablecell density is at or below a reference level. However, a level ofglucose of 2-3 g/L may be desired if the viable cell density is above areference level. For example, a reference density may be 1×10⁴ cells/mL,5×10⁴ cells/mL, 1×10⁵ cells/mL, 5×10⁵ cells/mL, 1×10⁶ cells/mL, 5×10⁶cells/mL, 1×10⁶ cells/mL, 5×10⁶ cells/mL,

In this example, the desired level of glucose depends on additional cellculture parameters, and both the level of glucose and the level of theadditional parameter (e.g., viable cell density) are evaluated prior tomaking a decision on the adjustment of the level of glucose (or anadditional parameter, such as the level of ammonium). It should furtherbe appreciated that algorithms may be used that can aid in determining,or determine, if a level needs to be adjusted. For instance, PartialLeast Squares (PLS) statistical methods can be used to build thecorrelations into predictive models. In some embodiments, the predictivemodels take into account the levels of glucose and viable cell densitypredictions and can be used to calculate the GUR (glucose uptake rate)of the system. The GUR is used to predict glucose consumption andtherefore determine how much nutrient feed is required to maintain thesystem around a given set point.

In some embodiments of methods provided herein, the level of glucose andthe one or more culture parameters are evaluated on an continuing basis,and the level of glucose and/or the one or more culture parameters areadjusted if the level of glucose and/or the one or more cultureparameters are outside a predetermined combination of ranges of glucoselevel and the one or more culture parameters. In some embodiments, thelevel of glucose and the additional parameters are evaluated every hour.Monitoring on a continuing basis includes continuous monitoring and/ormonitoring at regular intervals (e.g., once per minute, once per hour,twice per hour, daily, weekly, monthly, etc.)

It should be appreciated that methods provided herein allow for afeedback loop. In some embodiments, the level of glucose and,optionally, one or more additional parameters is determined and if thelevel of glucose and, optionally, one or more additional parameters isfound to be unsatisfactory, the level of glucose and, optionally, one ormore additional parameters is adjusted. In some embodiments, theadjustment is done automatically. For instance, if the level of glucoseis evaluated and found to be too low, a pump may be activated that addsadditional glucose to the culture medium. The monitoring of the levelsof glucose and, optionally, one or more additional parameters may bedone continuously. In some embodiments, the level of glucose and theadditional parameters are evaluated continuously (multiple time withinone minute), every minute, every 2 minutes, every 3 minutes, every 5minutes, every 10 minutes, every 20 minutes, every 30 minutes, everyhour, every 2 hours, or less frequently.

In one aspect, the disclosure provides an automated feedback system withone or more of the following elements: A data management system thatuses the following information flow to drive automation:

-   -   Constituent concentration >>Laser wavenumber shift >>Raman        collection system >>Raw Raman spectra>>Model application        system>>Predicted Raman value>>Consumption calculation (within        bioreactor interface) >>Feed required for maintenance >>Change        in feed (via pump speed, weight change, etc.)>>Change in        constituent concentration

For instance, a culture component (e.g., glucose) is measured usingRaman, the raw data is collected by the Raman system and transmitted toa model application system. Within the model application system the datatreatments of the predictive PLS model are applied to raw spectra andthe peaks are analyzed giving a predicted culture component value. Theprediction is sent to the bioreactor interface which uses it to as aninput for an algorithm which determines the consumption rate of theconstituent and calculates the rate at which a feed must be added inorder to maintain a specific concentration. The calculated pump speedwill change the addition rate which will increase or decrease theconcentration of the culture component as needed.

In some embodiments, multiple production reactors execute dozens ofbatches per year in an optimized run rate and high-throughput scale.Infrastructure and procedures are provided that support the volume ofdata, consistent addition of new data, and rolling manipulation frommodel to model and analyst to analyst. In some embodiments, Ramanspectra-based data load from a single batch comprises about threethousand Raman shift data points per spectra or more, multiplied byroughly one thousand time course spectra collected each run, totaling anestimated three million data values per batch.

In some embodiments, Raman measurements may be performed to evaluate thelevel of constituents in cultures or product yield from cell cultures(e.g., mammalian cell cultures). In some embodiments, Raman spectroscopymay be used for microbial fermentation applications for determining insitu process titer, glucose and ethanol (for example), as well assimultaneous multi-component prediction and monitoring. In someembodiments, Raman measurements may be performed ex situ on supernatantsamples (e.g., to evaluate product yield). In some embodiments, cellculture bioreactor monitoring may be used to evaluate in situ glucose,lactate, glutamine, glutamate, ammonium, VCD and TCD levels, amongothers. In some embodiments, set-point based control of glucose or otherculture components may be accomplished via application of a Ramanspectroscopy, e.g., using a derived PLS model.

In some embodiment, methods provided herein provide are useful foron-line monitoring and control of culture components (e.g., multiplecomponents in parallel). In some embodiments, monitoring and controlmethods disclosed herein permit control set-points to be established onnutrient and/or waste concentrations, for example. In some embodiments,monitoring and control methods disclosed herein provided hereineliminate the need for bolus-feed additions which can drastically swingchemical compositions. In some embodiments, monitoring and controlmethods disclosed herein may be used to manipulate cumulative cellularmetabolism by leveraging control set-point capability of nutrientcomponents to dampen metabolic waste product accumulations, establishingrobust continuous feed per cell implementations. In some embodiments,on-line monitoring provides information useful for batch level processtrending and modeling applications, such as, for example, SIMCA BatchOn-Line (SBOL) (Umetrics Inc., San Jose, Calif.), which permits rapididentification of batch-to-batch deviations to help improve processconsistency.

In one aspect, the disclosure provides methods for the analysis ofbiological samples. In some embodiments, the disclosure provides methodsfor evaluating a biological production process. In some embodiments, thedisclosure provides methods for evaluating a culture medium. In someembodiments, the disclosure provides methods for evaluating a culturecomponent level in a culture medium.

In some embodiments, analysis of a culture medium comprises determiningthe presence of one or more culture components in a biological sample.In some embodiments, analysis of a biological sample comprisesevaluating the level of a culture component. It should be appreciatedthat methods provided herein allow for the analysis of a wide variety ofculture media and biological samples. Culture media and biologicalsamples, as used herein, refer to media and samples that include one ormore components (e.g., glucose) of a biological production process. Forexample, a biological process may be the production of one or morebiological molecules in a cell production system. Biological moleculesmay be antibodies or other molecules (e.g., recombinant polypeptides).Components of a biological production process include sugars, aminoacids, peptides, proteins, nucleic acids, etc.

In some embodiments, evaluating a culture medium includes evaluating thepresence of one or more components (culture components) in a biologicalsample or culture medium. In some embodiments, evaluating a culturemedium includes evaluating the level of one or more components in abiological sample. In some embodiments, the presence or level of one ormore culture components can be correlated to the quality of the sampleand/or the progress of a particular biological manufacturing process.Culture components that can be analyzed according to methods providedherein include sugars (e.g., glucose), amino acids, nucleic acid, etc.For instance, for an optimal biological production process it may bedesired to have a specific level (e.g., concentration) of glucose to bepresent at the beginning of the biological production process.Determining the presence and/or the level of glucose than allows forevaluating a biological sample. Furthermore, as provided herein, if lessthan the desired level of glucose is present the level of glucose may beincreased by the addition of glucose solution.

In some embodiments, a Raman spectroscope is configured inline with abioreactor, vessel or fluid conduit of either one in order tonon-invasively (e.g., in a sterile fashion) monitor and/or determinelevels of culture components in the bioreactor or other vessel.

In some embodiments, the level of a component during the biologicalproduction process can be used to monitor the progress of the biologicalproduction process. Thus, for instance, if glucose is consumed during abiological production process, the presence of the same level of glucoseduring the progression of the biological production process as at thebeginning of the biological production process is a sign that thebioprocess is not proceeding as desired. In addition, the presence of anew component can be a sign that the biological production process isproceeding in some embodiments, or not proceeding in other embodiments,as planned. Thus, a biological production process may be monitored forthe occurrence of desired product or indicator that biologicalproduction process is progressing as desired. On the other hand, thepresence of a particular metabolite may be a sign that cells in thebiological production process are not generating the desired productbut, for instance, are merely proliferating. Thus, determining thepresence of one or more components in a biological sample is a way ofevaluating the sample and predicting the successfulness (e.g., yield) ofa biological production process.

It should be appreciated that the component analysis can also beexpanded to multiple components. Thus, for instance, a biologicalproduction process may require a particular ratio of glucose toglutamate to proceed optimally. A sample may be monitored prior to orthroughout the reaction for this relationship and the conditions may beadjusted if the observed ratio deviates from the desired ratio.

In one aspect, the disclosure provides methods for evaluating abiological sample by generating a reference library of Raman signaturesof culture components that are associated with a sample with aparticular outcome (e.g., if a particular component is not aggregated oroxidized). For instance, Raman signatures can be generated fromcomponents in samples that are known to result in a biologicalproduction process with a good yield and Raman signatures can begenerated from samples that are associated with a low yield (e.g., wherethe Raman spectrum would show undesired degradation of a particularcomponent). A Raman spectrum can subsequently be taken from an unknownsample and be parsed with the library of Raman signatures

In some embodiments, the herein-described models and Raman spectracollected from culture medium may be used to optimize the culture mediumfor biological production. The cell growth may be, for example, forprotein production (e.g., for antibody production, for example forhumanized antibody production). In some embodiments, cell growth may bethat of a recombinant cell (e.g., bacterial, yeast, mammalian or othercell type) that expresses a protein of interest. In some embodiments, aprotein of interest may be, but is not limited to, anti-LINGO,anti-LINGO-1, interferon (e.g., interferon beta 1a-AVONEX), Abciximab(REOPRO®), Adalimumab (HUMIRA®), Alemtuzumab (CAMPATH®), Basiliximab(SIMULECT®), Bevacizumab (AVASTIN®), Cetuximab (ERBITUX®), Certolizumabpegol (CIMZIA®), Daclizumab (ZENAPAX®), Eculizumab (SOLIRISC),Efalizumab (RAPTIVA®), Gemtuzumab (MYLOTARG®), Ibritumomab tiuxetan(ZEVALIN®), Infliximab (REMICADE®), Muromonab-CD3 (ORTHOCLONE OKT3®),Natalizumab (TYSABRI®), Omalizumab (XOLAIR®), Palivizumab (SYNAGIS®),Panitumumab (VECTIBIX®), Ranibizumab (LUCENTIS®), Rituximab (RITUXAN®),Tositumomab (BEXXAR®), and/or Trastuzumab (HERCEPTIN®). In someembodiments, the protein of interest is Natalizumab (TYSABRI®). In someembodiments, the protein of interest is a blood cascade protein. Bloodcascade proteins are known in the art and include, but are not limitedto, Factor VII, tissue factor, Factor IX, Factor X, Factor XI, FactorXII, Tissue factor pathway inhibitor, Factor V, prothrombin, thrombin,vonWillebrandFactor, kininigen, prekallikrien, kallikrein, fribronogen,fibrin, protein C, thrombomodulin, and antithrombin. In someembodiments, the blood cascade protein is Factor IX or Factor VIII. Itshould be appreciated that methods provided herein are also applicablefor uses involving the production of versions of blood cascade proteins,including blood cascade proteins that are covalently bound to antibodiesor antibody fragments, such as Fc. In some embodiments, the bloodcascade protein is Factor IX-Fc (FIXFc) or Factor VIII-Fc (FVIIIFc). Insome embodiments, one or more proteins of interest are hormones,regulatory proteins and/or neurotrophic factors. Neurotrophic factorsare known in the art and include nerve growth factor (NGF),brain-derived neurotrophic factor (BDNF), neurotrophin-3 (NT-3),neurotrophin-4 (NT-4), members of the glial cell line-derivedneurotrophic factor ligands (GDNF) and ciliary neurotrophic factor(CNTF). In some embodiments, the protein of interest is neublastin.

In some embodiments, a protein of interest may be, but is not limitedto, Abagovomab, Abciximab, Actoxumab, Adalimumab, Adecatumumab,Afelimomab, Afutuzumab, Alacizumab pegol, ALD, Alemtuzumab, Alirocumab,Altumomab pentetate, Amatuximab, Anatumomab mafenatox, Anrukinzumab,Apolizumab, Arcitumomab, Aselizumab, Atinumab, Atlizumab, Atorolimumab,Bapineuzumab, Basiliximab, Bavituximab, Bectumomab, Belimumab,Benralizumab, Bertilimumab, Besilesomab, Bev acizumab, Bezlotoxumab,Biciromab, Bimagrumab, Bivatuzumab mertansine, Blinatumomab, Blosozumab,Brentuximab vedotin, Briakinumab, Brodalumab, Canakinumab, Cantuzumabmertansine, Cantuzumab ravtansine, Caplacizumab, Capromab pendetide,Carlumab, Catumaxomab, Cedelizumab, Certolizumab pegol, Cetuximab,Citatuzumab bogatox, Cixutumumab, Clazakizumab, Clenoliximab,Clivatuzumab tetraxetan, Conatumumab, Concizumab, Crenezumab,Dacetuzumab, Daclizumab, Dalotuzumab, Daratumumab, Demcizumab,Denosumab, Detumomab, Dorlimomab aritox, Drozitumab, Duligotumab,Dupilumab, Dusigitumab, Ecromeximab, Eculizumab, Edobacomab,Edrecolomab, Efalizumab, Efungumab, Eldelumab, Elotuzumab, Elsilimomab,Enavatuzumab, Enlimomab pegol, Enokizumab, Enoticumab, Ensituximab,Epitumomab cituxetan, Epratuzumab, Erlizumab, Ertumaxomab, Etaracizumab,Etrolizumab, Evolocumab, Exbivirumab, Fanolesomab, Faralimomab,Farletuzumab, Fasinumab, FBTA, Felvizumab, Fezakinumab, Ficlatuzumab,Figitumumab, Flanvotumab, Fontolizumab, Foralumab, Foravirumab,Fresolimumab, Fulranumab, Futuximab, Galiximab, Ganitumab, Gantenerumab,Gavilimomab, Gemtuzumab ozogamicin, Gevokizumab, Girentuximab,Glembatumumab vedotin, Golimumab, Gomiliximab, Guselkumab, Ibalizumab,Ibritumomab tiuxetan, Icrucumab, Igovomab, Imciromab, Imgatuzumab,Inclacumab, Indatuximab ravtansine, Infliximab, Intetumumab, Inolimomab,Inotuzumab ozogamicin, Ipilimumab, Iratumumab, Itolizumab, Ixekizumab,Keliximab, Labetuzumab, Lampalizumab, Lebrikizumab, Lemalesomab,Lerdelimumab, Lexatumumab, Libivirumab, Ligelizumab, Lintuzumab,Lirilumab, Lodelcizumab, Lorvotuzumab mertansine, Lucatumumab,Lumiliximab, Mapatumumab, Margetuximab, Maslimomab, Mavrilimumab,Matuzumab, Mepolizumab, Metelimumab, Milatuzumab, Minretumomab,Mitumomab, Mogamulizumab, Morolimumab, Motavizumab, Moxetumomabpasudotox, Muromonab-CD, Nacolomab tafenatox, Namilumab, Naptumomabestafenatox, Narnatumab, Natalizumab, Nebacumab, Necitumumab,Nerelimomab, Nesvacumab, Nimotuzumab, Nivolumab, Nofetumomab merpentan,Ocaratuzumab, Ocrelizumab, Odulimomab, Ofatumumab, Olaratumab,Olokizumab, Omalizumab, Onartuzumab, Oportuzumab monatox, Oregovomab,Orticumab, Otelixizumab, Oxelumab, Ozanezumab, Ozoralizumab,Pagibaximab, Palivizumab, Panitumumab, Panobacumab, Parsatuzumab,Pascolizumab, Pateclizumab, Patritumab, Pemtumomab, Perakizumab,Pertuzumab, Pexelizumab, Pidilizumab, Pinatuzumab vedotin, Pintumomab,Placulumab, Polatuzumab vedotin, Ponezumab, Priliximab, Pritoxaximab,Pritumumab, Quilizumab, Racotumomab, Radretumab, Rafivirumab,Ramucirumab, Ranibizumab, Raxibacumab, Regavirumab, Reslizumab,Rilotumumab, Rituximab, Robatumumab, Roledumab, Romosozumab,Rontalizumab, Rovelizumab, Ruplizumab, Samalizumab, Sarilumab, Satumomabpendetide, Secukinumab, Seribantumab, Setoxaximab, Sevirumab,Sibrotuzumab, Sifalimumab, Siltuximab, Simtuzumab, Siplizumab,Sirukumab, Solanezumab, Solitomab, Sonepcizumab, Sontuzumab, Stamulumab,Sulesomab, Suvizumab, Tabalumab, Tacatuzumab tetraxetan, Tadocizumab,Talizumab, Tanezumab, Taplitumomab paptox, Tefibazumab, Telimomabaritox, Tenatumomab, Teneliximab, Teplizumab, Teprotumumab, TGN,Ticilimumab , Tildrakizumab, Tigatuzumab, TNX-, Tocilizumab ,Toralizumab, Tositumomab, Tovetumab, Tralokinumab, Trastuzumab, TRBS,Tregalizumab, Tremelimumab, Tucotuzumab celmoleukin, Tuvirumab,Ublituximab, Urelumab, Urtoxazumab, Ustekinumab, Vantictumab,Vapaliximab, Vatelizumab, Vedolizumab, Veltuzumab, Vepalimomab,Vesencumab, Visilizumab, Volociximab, Vorsetuzumab mafodotin, Votumumab,Zalutumumab, Zanolimumab, Zatuximab, Ziralimumab and/or Zolimomabaritox.

Computer Implementations

It should be appreciated that methods disclosed herein may beimplemented in any of numerous ways. For example, certain embodimentsmay be implemented using hardware, software or a combination thereof.When implemented in software, the software code can be executed on anysuitable processor or collection of processors, whether provided in asingle computer or distributed among multiple computers. Such processorsmay be implemented as integrated circuits, with one or more processorsin an integrated circuit component. Though, a processor may beimplemented using circuitry in any suitable format.

Further, it should be appreciated that a computer may be embodied in anyof a number of forms, such as a rack-mounted computer, a desktopcomputer, a laptop computer, or a tablet computer. Additionally, acomputer may be embedded in a device not generally regarded as acomputer but with suitable processing capabilities, including a smartphone, tablet, or any other suitable portable or fixed electronicdevice.

Also, a computer may have one or more input and output devices. Thesedevices can be used, among other things, to present a user interface.Examples of output devices that can be used to provide a user interfaceinclude printers or display screens for visual presentation of outputand speakers or other sound generating devices for audible presentationof output. Examples of input devices that can be used for a userinterface include keyboards, and pointing devices, such as mice, touchpads, and digitizing tablets.

Such computers may be interconnected by one or more networks in anysuitable form, including as a local area network or a wide area network,such as an enterprise network or the Internet. Such networks may bebased on any suitable technology and may operate according to anysuitable protocol and may include wireless networks, wired networks orfiber optic networks.

Also, the various methods or processes outlined herein may be coded assoftware that is executable on one or more processors that employ anyone of a variety of operating systems or platforms. Additionally, suchsoftware may be written using any of a number of suitable programminglanguages and/or programming or scripting tools (e.g., MATLAB), and alsomay be compiled as executable machine language code or intermediate codethat is executed on a framework or virtual machine.

In this respect, aspects of the disclosure may be embodied as a computerreadable medium (or multiple computer readable media) (e.g., a computermemory, one or more floppy discs, compact discs (CD), optical discs,digital video disks (DVD), magnetic tapes, flash memories, circuitconfigurations in Field Programmable Gate Arrays or other semiconductordevices, or other non-transitory, tangible computer storage medium)encoded with information (e.g., Raman signature information) and/or oneor more programs that, when executed on one or more computers or otherprocessors, perform methods that implement the various embodiments ofthe disclosure discussed above. The computer readable medium or mediacan be transportable, such that the program or programs stored thereoncan be loaded onto one or more different computers or other processorsto implement various aspects of the present disclosure as discussedabove. As used herein, the term “non-transitory computer-readablestorage medium” encompasses only a computer-readable medium that can beconsidered to be a manufacture (e.g., article of manufacture) or amachine.

The terms “program” or “software” are used herein in a generic sense torefer to any type of computer code or set of computer-executableinstructions that can be employed to program a computer or otherprocessor to implement various aspects of the present disclosure asdiscussed above. Additionally, it should be appreciated that accordingto one aspect of this embodiment, one or more computer programs thatwhen executed perform methods of the present disclosure need not resideon a single computer or processor, but may be distributed in a modularfashion amongst a number of different computers or processors toimplement various aspects of the present disclosure.

As used herein, the term “database” generally refers to a collection ofdata arranged for ease and speed of search and retrieval. Further, adatabase typically comprises logical and physical data structures. Thoseskilled in the art will recognize methods described herein may be usedwith any type of database including a relational database, anobject-relational database and an XML-based database, where XML standsfor “eXtensible-Markup-Language”. For example, Raman spectra informationmay be stored in and retrieved from a database. The

Raman spectra information may be stored in or indexed in a manner thatrelates culture component levels (e.g., glucose levels) or bioreactorconditions, or with a variety of other relevant information.

Computer-executable instructions may be in many forms, such as programmodules, executed by one or more computers or other devices. Generally,program modules include routines, programs, objects, components, datastructures, etc. that perform particular tasks (e.g., tasks relating toFeedback control) or implement particular abstract data types. Typicallythe functionality of the program modules may be combined or distributedas desired in various embodiments.

The present disclosure is further illustrated by the following Examples,which in no way should be construed as further limiting. The entirecontents of all of the references (including literature references,issued patents, published patent applications, and co pending patentapplications) cited throughout this application are hereby expresslyincorporated by reference, in particular for the teaching that isreferenced hereinabove.

The present disclosure is further illustrated by the following Examples,which in no way should be construed as further limiting. The entirecontents of all of the references (including literature references,issued patents, published patent applications, and co-pending patentapplications) cited throughout this application are hereby expresslyincorporated by reference, in particular for the teaching that isreferenced hereinabove.

EXAMPLES

Raman spectroscopy was used for real time prediction of parametersincluding nutrient, metabolic waste and cell growth parameters within amammalian cell culture setting. Components such as glucose, lactate,glutamate, ammonia and osmolality were robust in validation batchpredictions and cross-scale applications.

Example 1 Materials and Methods Bioreactor Cultures

Twenty cell culture process runs across three different stirred-tankbioreactor scales were used: 5 L Applikon (Applikon Biotechnology, TheNetherlands) bench-scale glass bioreactors (n=13), 200 L custom ABEC(ABEC Inc., Bethlehem, PA) pilot-scale stainless steel bioreactors(n=4), and 2000 L custom ABEC manufacturing-scale stainless steelbioreactors (n=3). Here, a 13 day fed-batch process producing amonoclonal antibody via a CHO cell line is described. The processutilizes a proprietary chemically defined basal media solution withfixed amounts of daily nutrient feed (also chemically defined) and anoption to add additional glucose stock if necessary, based on batchperformance. The pH, temperature, and dissolved oxygen were controlled.

Bench-scale 5 L runs varied key process parameters to build processrobustness into the dataset including +10% high volume feed (n=2), -10%low volume feed (n=2), +25% high initial seed density (n=3), and -25%low initial seed density (n=4). The dataset incorporated two controlcondition batches from the 5 L scale as well. All pilot andmanufacturing-scale runs served as control process runs contributingscale-specific data into the model while applying traditional scale-upparameters such as power per volume and k_(L)a mass transfer to theselection of agitation and aeration strategies.

Cell Culture Sample Collection and Offline Analysis

Bench-scale runs were manually sampled a minimum of twice per day. Twoof the pilot-scale runs were sampled six times per day by a BaychroMAT®inline automated sampling device (Bayer Technology Services GmbH,Leverkusen, Germany) coupled with an inline Cedex HiRes cell counter(Roche Custom Biotech, Roche Diagnostics, Indianapolis, IN) for celldensity and viability analysis followed by inline 0.22 μm sterilefiltering apparatus ultimately leading to a 2-8° C. chilled fractioncollector. Two additional pilot-scale runs, as well asmanufacturing-scale runs, were sampled manually three times per day. Allsamples were run on a Nova Biomedical BioProfile Flex metaboliteanalyzer (Nova Biomedical Corporation, Waltham, Mass.) measuringglucose, lactate, glutamate, ammonium, and osmolality. Cell density andviability data were measured for each sample using a Cedex cell counter(Roche Custom Biotech, Roche Diagnostics, Indianapolis, Ind.) involvinga trypan blue exclusion assay except for the two pilot-scale runs, whichutilized the inline Cedex HiRes instrument which employed the sametrypan blue exclusion method but has a higher resolution camera than theCedex instrument. The culture samples across the three scales wereanalyzed by the cell counting and metabolite analyzer equipmentimmediately upon collection except in the case of the two pilot-scalebatches in which the automatic sampling device operated by pullingsamples twenty four hours a day at an interval of 6 hours. Thecontinuous nature of the auto-sampling device allowed for overnightsampling, filtration, and temperature controlled storage of the samples.On a daily basis, collected samples were analyzed on the BioProfile Flexinstrument for metabolite measurements.

Collection of Raman Spectra

Raman spectra representing inelastic photon scattering data across a150-3425 cm-1 wavenumber range were collected in situ during all processruns using a Kaiser RamanRxn2 analyzer (Kaiser Optical Systems Inc., AnnArbor, MI). The base unit was comprised of an Invictus 785 nm laserexcitation, an axial transmissive spectrograph with a HoloPlextransmission grating (Kaiser Optical Systems, Inc., Ann Arbor, Mich.),and a cooled charged-coupled device (CCD) detector maintained at −40° C.The base unit was connected to the sample location using fiber opticsand application-optimized Raman probes. Bench-scale runs utilizedbIO-LAB-220 (Kaiser Optical Systems Inc., Ann Arbor, Mich.) top-mountedvertical stainless steel immersion probes inserted in the glass vesselswith standard threaded adaptors and aseptic boundary gaskets whereaspilot and manufacturing-scale runs utilized bIO-PRO (Kaiser OpticalSystems Inc., Ann Arbor, Mich.) stainless steel immersion probes securedwith Ingold connections. Raman probes contained the same sapphire windowmaterial. Spectral acquisitions utilizing cosmic ray removal andintensity correction, both of which are standard options within iC Raman4.1 Software (Mettler Toledo Autochem, Columbia, Md.), consisted of theco-addition of 600 individual spectral acquisitions at 1 second ofexposure time each, resulting in 10 minutes total exposure time perspectrum.

Spectral Preprocessing and Multivariate Modeling

Univariate-Y, multivariate-X predictive component models were built fromspectral data collected during fed-batch cell culture processing withinbioreactors at the three respective scales; bench-scale (5 L),pilot-scale (200 L) and manufacturing-scale (2000 L). No spectral datacollected off line or via reference solutions of chemically definedmedia or nutrient solutions were employed. Spectra exported from theKaiser RamanRxn2 instrument as .spc files were aligned with offlineinstrument data in a spreadsheet format that served as an input file forSIMCA 12 (Umetrics Inc., San Jose, Calif.). Within SIMCA, mathematicalpre-processing methods were applied to all X-block cell culture spectraincluding 1^(st) derivative Savitzky-Golay smoothing with 15cm⁻¹ pointspacing and a quadratic polynomial followed by Standard Normal Variate(SNV), and mean-centering. Y-block reference component data was treatedwith unit-variance scaling (UV). SIMCA was then used for multivariateanalysis and partial least square (PLS) modeling to find a correlationbetween X and Y blocks on a component by component basis for allmeasured values from the Cedex cell counting and Nova Flex metaboliteanalyzer instruments. Y-block reference data outliers were identifiedand removed through analysis of scores plots, loading plots, residualplots, and Hotelling's T²plots within SIMCA. The variable influence onprojection (VIP) algorithm, which ranks the importance of theX-variables (spectral regions) taking into consideration the amount ofexplained variation in the Y-component variance (offline measurements),was calculated within SIMCA software and used to determine relativecorrelation of spectral regions throughout the full Raman shift rangewith offline data. Spectral region selection criteria were set atVIP >1.0 as terms with larger VIP values, especially larger than 1.0,are the most significant in correlation with Y-block data. An example ofthe process flow used to generate the PLS models is illustrated in FIG.1,which shows the process flow diagram for data preprocessing,multivariate model building, optimization, and selection criteria forgenerated PLS models. This guideline includes the types and sequence ofdata preprocessing and normalization in addition to the iterativeoutlier removal and region selection rules followed. The criteriadetermining whether an optimized model was reached within the modelbuilding process was strategically focused solely on minimizing the rootmean squared error of prediction (RMSEP) of the validation dataset ofinterest with a two pass iteration limitation placed on each of thepossible outlier removal steps to support a rapid pace of modeldevelopment.

Example 2 Scale-Specific Modeling

An assessment of the scale-up predictability of the technology wasperformed using six dataset combinations targeting permutations of theprocess scales (5 L, 200 L, and 2,000 L) to generate PLS calibrationmodels which were evaluated while targeting a manufacturing-scale batchas a validation data set.

For each offline measured Y-block component, calibration data sets werecreated using the following scenarios: bench-scale data only,pilot-scale data only, bench-scale plus pilot-scale data, bench-scaleplus pilot-scale data plus manufacturing-scale data. In one scenario,three iterations were created in order to use each of the threemanufacturing data sets as an external validation set while includingthe other two. PLS model calibration statistics of these six designs,tracking parameters such as number of sample observations used (toreflect the size of the calibration models as well as the extent ofdataset reduction resulting from outlier removal steps during modelgeneration), number of PLS model factors, R2 (the percent of variationof Y-block reference data explained by the model), and Q2 (the percentof variation of Y-block reference data predicted by the model accordingto a leave-one-sample-out style internal cross validation) areillustrated in Table 1. An example of PLS model validation results,illustrated in Table 2, where each of the six calibration models,outlined in Table 1, were used to predict a single manufacturing-scalebatch. The validation parameters tabulated include root mean squarederror of estimation (RMSEE), root mean squared error of cross validation(RMSECV), root mean squared error of prediction (RMSEP) and averagepercent error between measured and predicted values.

Approximately one thousand Raman spectra were captured during amanufacturing batch, illustrated in FIGS. 2A-C, in different forms. Inone form, FIG. 2A, the raw spectra illustrate that shift is relativelyminimal during the first six days of the process but increases at afaster rate following day 6. In another form, FIG. 2B, the 1stderivative preprocessed spectra removes much of the intensity variationacross the full spectrum, and in yet another form, FIG. 2C, the 1stderivative, Savitzky-Golay and SNV preprocessed spectra lead to furthernormalization.

Time course prediction trends of a manufacturing batch using acalibration model built from bench-scale, pilot-scale, and twomanufacturing batches were used to evaluate; VCD, TCD, glucose, lactate,glutamate, ammonium, and osmolality. PLS model prediction results wereobtained from calibration data containing bench-scale, pilot-scale, andmanufacturing-scale (batch 1 and 2) predicting a single 2,000 Lmanufacturing-scale validation batch (batch 3) for VCD (FIG. 3A), TCD(FIG. 3B), glucose (FIG. 3C), lactate (FIG. 3D), glutamate (FIG. 3E),ammonium (FIG. 3F), and osmolality (FIG. 3G). In FIGS. 3A-G,square-dotted trends represent measured values (actual), black trendsrepresent PLS model predictions, and gray error bars around all blackprediction trends visualize PLS model root mean squared error ofprediction (RMSEP) for each component model, a fixed value error. Thesquare-dotted trend lines (actual) of FIGS. 3A and 3B contain error barsaround measured VCD and TCD values, indicating an assumed 10%measurement variability of Cedex cell counter, a fixed percent error.

The root mean squared error of prediction (RMSEP) was determined as apercentage of maximum process value for each component to assesspredictive performance of the models across different single scales andcombinations of scales. As illustrated in FIG. 4, glucose, lactate andosmolality models predicted at or below 10% relative RMSEP regardless ofthe combination of scale-specific data that were built in.

PLS models were generated and used to analyze effectiveness incross-scale models such as in the VCD and TCD prediction capabilitieswhere it may be advantageous to include at-scale data to decrease theprediction error for the manufacturing-scale validation batch. Thus, insome embodiments, the components of viable cell density (VCD) and totalcell density (TCD) were predicted with at-scale manufacturing dataincluded into the model. In one embodiment, relative RMSEP trends forglutamate and ammonium models, as shown in FIG. 4, demonstrate a levelof scale-dependency similar to those observed in VCD and TCD. Evaluationof the concentration range captured for glutamate and ammoniumcomponents by each scale's set of runs, as illustrated in the observedvs. predicted linearity trends of FIGS. 5E-F, show that themanufacturing runs had minimum and maximum values similar to pilot-scaleruns but stretch out wider than the minimum and maximum values capturedin small scale runs. In some embodiments, therefore, it is desirable touse models based on a range of concentration values of particularcomponents that will be experienced in the prediction context (e.g., ata manufacturing-scale).

Example 3 Lactate Assessment

Raman spectroscopy was used for prediction of lactate levels in abioreactor culture. Sodium L-lactate solution, procured from SIGMA (>99%sodium L-lactate P/N 71718), was spiked into chemically defined cellculture media. The L-Lactate is representative of the lactate secretedby our cells, as opposed to D-Lactate. Sodium L-lactate was used insteadof lactic acid so only an increase in sodium ions rather than hydroxidewas created, which would affect the pH controlled 3 L bioreactor vessel.

The experiment was executed in a 3 L glass bioreactor vessel at processtemperature (37C) , controlled between 6.9-7.0 pH, using cell freechemically defined basal media, and Raman probes installed. The spikingsolution used was the same chemically defined basal media formulationwith 20g/L of the sodium L-lactate solution added into the formulationto minimize the amount of dilution occurring with other components inthe basal media. Raw Raman spectra were collected between 420-1600(cm-1) wavenumber (FIG. 6A), and between 2750-3400 (cm-1) wavenumber(FIG. 6C). The spectra were signal processed and normalized between420-1600 (cm-1) wavenumber (FIG. 6B), and between 2750-3400 (cm-1)wavenumber (FIG. 6D).

Tables

TABLE 1 Summary of a PLS model calibration. The column headers indicatedifferent combinations of scale-specific calibration models. In thethree cases where manufacturing data was built into calibration models,only two out of three batches were used to allow the use of one batch asa validation dataset, which allows for three possible combinations.Bench + Bench + Bench + Bench + Pilot + MFG Pilot + MFG Pilot + MFGBench-scale Pilot-scale Pilot-scale 1&2 1&3 2&3 Number of CalibrationSamples Used Glucose 226 142 475 569 560 592 Lactate 244 211 413 584 516536 Glutamate 177 219 497 597 595 585 Ammonium 276 236 518 604 579 571Osmolality 131 234 330 431 400 402 VCD 324 147 461 562 638 517 TCD 385155 511 555 554 510 Number of PLS Model Factors Glucose 4 3 5 4 5 5Lactate 7 6 7 8 6 9 Glutamate 3 5 8 10 10 8 Ammonium 8 8 8 7 9 9Osmolality 6 4 5 5 6 7 VCD 3 3 4 4 4 4 TCD 5 4 5 3 3 3 R² Glucose 0.9420.936 0.926 0.875 0.918 0.903 Lactate 0.93 0.92 0.862 0.888 0.907 0.946Glutamate 0.93 0.88 0.83 0.853 0.81 0.833 Ammonium 0.88 0.96 0.82 0.830.88 0.89 Osmolality 0.97 0.74 0.91 0.86 0.86 0.87 VCD 0.967 0.978 0.970.97 0.966 0.974 TCD 0.97 0.99 0.97 0.96 0.97 0.97 σ² Glucose 0.93 0.9270.917 0.869 0.909 0.892 Lactate 0.91 0.88 0.842 0.862 0.888 0.929Glutamate 0.92 0.81 0.78 0.794 0.73 0.788 Ammonium 0.88 0.93 0.72 0.780.83 0.84 Osmolality 0.93 0.68 0.88 0.85 0.83 0.83 VCD 0.957 0.972 0.9650.966 0.962 0.972 TCD 0.96 0.98 0.97 0.96 0.96 0.97

TABLE 2 Results of PLS model validation. Manufacturing batch 3 was usedas the validation batch for bench-scale only, pilot-scale only, andbench-scale + pilot-scale calibration data sets. The manufacturingvalidation batch used for calibration models is built withmanufacturing-scale data is the batch not indicated in the columnheader. Bench + Bench + Bench + Bench + Pilot + MFG Pilot + MFG Pilot +MFG Bench-scale Pilot-scale Pilot-scale 1&2 1&3 2&3 Root Mean SquareError of Estimation (RMSEE) Bench-scale Glucose (g/L) 0.58 0.52 0.730.88 0.72 0.83 Lactate (g/L) 0.12 0.13 0.17 0.16 0.14 0.12 Glutamate0.11 0.2 0.29 0.28 0.32 0.3 (mM) Ammonium 0.24 0.23 0.35 0.38 0.3 0.29(mM) Osmolality 8.25 15.92 11.24 13.93 12.91 12.81 (mOsm/kg) VCD (×10⁶1.55 1.3 1.46 1.48 1.58 1.3 vc/mL) TCD (×10⁶ 1.86 1.13 1.64 1.78 1.721.48 tc/mL) Root Mean Square Error of Cross Validation (RMSECV)Bench-scale Glucose (g/L) 0.65 0.55 0.78 0.88 0.77 0.89 Lactate (g/L)0.14 0.18 0.17 0.18 0.16 0.14 Glutamate 0.12 0.27 0.33 0.34 0.39 0.34(mM) Ammonium 0.29 0.35 0.45 0.46 0.38 0.37 (mM) Osmolality 12.78 17.7413.06 14.76 14.46 14.3 (mOsm/kg) VCD (×10⁶ 1.76 1.48 1.58 1.58 1.69 1.37vc/mL) TCD (×10⁶ 2.22 1.33 1.85 1.81 1.76 1.51 tc/mL) Root Mean SquareError of Prediction (RMSEP) Bench-scale Glucose (g/L) 1.13 77 0.74 0.60.74 0.91 Lactate (g/L) 0.19 0.2 0.19 0.18 0.16 0.16 Glutamate 1.18 0.410.57 0.33 0.34 0.32 (mM) Ammonium 1.21 1.07 0.55 0.47 0.32 0.42 (mM)Osmolality 23.2 19.5 20.8 10.7 14.6 13.1 (mOsm/kg) VCD (×10⁶ 4.87 5.755.57 1.3 1.27 1.46 vc/mL) TCD (×10⁶ 3.68 3.72 2.95 1.64 2.35 1.71 tc/mL)Average % Error Glucose 10.73 7.14 6.57 5.94 6.1 9.3 Lactate 15.17 17.1718.9 17.28 14.7 15.66 Glutamate 74.92 26.17 35.7 29.46 15.6 34.51Ammonium 19.1 46.93 14.15 14.96 12.1 13.19 Osmolality 6.4 5.16 5.5 2.342.6 3.37 VCD 60.87 66.57 59.15 22.87 11.7 18.3 TCD 57.46 23.56 38.6421.16 28.3 21.59

While several embodiments of the present disclosure have been describedand illustrated herein, those of ordinary skill in the art will readilyenvision a variety of other means and/or structures for performing thefunctions and/or obtaining the results and/or one or more of theadvantages described herein, and each of such variations and/ormodifications is deemed to be within the scope of the presentdisclosure. More generally, those skilled in the art will readilyappreciate that all parameters, dimensions, materials, andconfigurations described herein are meant to be exemplary and that theactual parameters, dimensions, materials, and/or configurations willdepend upon the specific application or applications for which theteachings of the present disclosure is/are used. Those skilled in theart will recognize, or be able to ascertain using no more than routineexperimentation, many equivalents to the specific embodiments of thedisclosure described herein. It is, therefore, to be understood that theforegoing embodiments are presented by way of example only and that,within the scope of the appended claims and equivalents thereto, thedisclosure may be practiced otherwise than as specifically described andclaimed. The present disclosure is directed to each individual feature,system, article, material, and/or method described herein. In addition,any combination of two or more such features, systems, articles,materials, and/or methods, if such features, systems, articles,materials, and/or methods are not mutually inconsistent, is includedwithin the scope of the present disclosure.

The indefinite articles “a” and “an,” as used herein in thespecification and in the claims, unless clearly indicated to thecontrary, should be understood to mean “at least one.”

The phrase “and/or,” as used herein in the specification and in theclaims, should be understood to mean “either or both” of the elements soconjoined, e.g., elements that are conjunctively present in some casesand disjunctively present in other cases. Other elements may optionallybe present other than the elements specifically identified by the“and/or” clause, whether related or unrelated to those elementsspecifically identified unless clearly indicated to the contrary. Thus,as a non-limiting example, a reference to “A and/or B,” when used inconjunction with open-ended language such as “comprising” can refer, inone embodiment, to A without B (optionally including elements other thanB); in another embodiment, to B without A (optionally including elementsother than A); in yet another embodiment, to both A and B (optionallyincluding other elements); etc.

As used herein in the specification and in the claims, “or” should beunderstood to have the same meaning as “and/or” as defined above. Forexample, when separating items in a list, “or” or “and/or” shall beinterpreted as being inclusive, e.g., the inclusion of at least one, butalso including more than one, of a number or list of elements, and,optionally, additional unlisted items. Only terms clearly indicated tothe contrary, such as “only one of” or “exactly one of,” or, when usedin the claims, “consisting of,” will refer to the inclusion of exactlyone element of a number or list of elements. In general, the term “or”as used herein shall only be interpreted as indicating exclusivealternatives (e.g. “one or the other but not both”) when preceded byterms of exclusivity, such as “either,” “one of,” “only one of,” or“exactly one of.” “Consisting essentially of,” when used in the claims,shall have its ordinary meaning as used in the field of patent law.

As used herein in the specification and in the claims, the phrase “atleast one,” in reference to a list of one or more elements, should beunderstood to mean at least one element selected from any one or more ofthe elements in the list of elements, but not necessarily including atleast one of each and every element specifically listed within the listof elements and not excluding any combinations of elements in the listof elements. This definition also allows that elements may optionally bepresent other than the elements specifically identified within the listof elements to which the phrase “at least one” refers, whether relatedor unrelated to those elements specifically identified. Thus, as anon-limiting example, “at least one of A and B” (or, equivalently, “atleast one of A or B,” or, equivalently “at least one of A and/or B”) canrefer, in one embodiment, to at least one, optionally including morethan one, A, with no B present (and optionally including elements otherthan B); in another embodiment, to at least one, optionally includingmore than one, B, with no A present (and optionally including elementsother than A); in yet another embodiment, to at least one, optionallyincluding more than one, A, and at least one, optionally including morethan one, B (and optionally including other elements); etc.

In the claims, as well as in the specification above, all transitionalphrases such as “comprising,” “including,” “carrying,” “having,”“containing,” “involving,” “holding,” and the like are to be understoodto be open-ended, e.g., to mean including but not limited to. Only thetransitional phrases “consisting of” and “consisting essentially of”shall be closed or semi-closed transitional phrases, respectively, asset forth in the United States Patent Office Manual of Patent ExaminingProcedures, Section 2111.03.

Use of ordinal terms such as “first,” “second,” “third,” etc., in theclaims to modify a claim element does not by itself connote anypriority, precedence, or order of one claim element over another or thetemporal order in which acts of a method are performed, but are usedmerely as labels to distinguish one claim element having a certain namefrom another element having a same name (but for use of the ordinalterm) to distinguish the claim elements.

What is claimed is:
 1. A method of assessing a bioreactor culture, themethod comprising: (i) obtaining a Raman spectrum of amanufacturing-scale bioreactor culture; and (ii) determining a cultureparameter of the manufacturing-scale bioreactor culture using a modelthat relates the Raman spectrum to the culture parameter, wherein themodel is developed based on one or more test bioreactor cultures of asmaller volume than the manufacturing-scale bioreactor culture.
 2. Themethod of claim 1, wherein the volume of the manufacturing-scalebioreactor culture is in a range of 1000 L to 4000 L.
 3. The method ofclaim 1, wherein the volume of the test bioreactor culture is in a rangeof 1 L to 400 L. 4-7. (canceled)
 8. The method of claim 1, wherein themodel is a partial least squares model.
 9. The method of claim 1,wherein the culture parameter is a level of glucose, glutamate, ammoniaor lactate in the culture. 10-13. (canceled)
 14. The method of claim 1,wherein the culture parameter is the osmolality of the culture. 15-17.(canceled)
 18. A method of assessing a bioreactor culture, the methodcomprising: (i) obtaining a Raman spectrum of a manufacturing-scalebioreactor culture; and (ii) determining a culture parameter of themanufacturing-scale bioreactor culture using a model that relates theRaman spectrum to the culture parameter, wherein the model is developedbased on at least one bioreactor culture of a smaller volume than themanufacturing-scale bioreactor culture and at least one bioreactorculture of substantially the same volume as the manufacturing-scalebioreactor culture.
 19. The method of claim 18, wherein themanufacturing-scale bioreactor culture is in a range of 1000 L to 4000L.
 20. The method of claim 18, wherein the at least one bioreactorculture of a smaller volume than the manufacturing-scale bioreactorculture is in a range of 1 L to 100 L.
 21. The method of claim 18,wherein the Raman spectrum comprises spectral signal in the 200 cm⁻¹wavenumber range. 22-23. (canceled)
 24. The method of claim 18, whereinthe Raman spectrum is obtained using a Raman analyzer configured with alaser or other suitable light source that operates at wavelengths in arange of 325 nm to 1064 nm.
 25. A bioreactor system comprising: abioreactor chamber configured for containing a manufacturing-scalebioreactor culture; a probe configured for obtaining a Raman spectrum ofthe manufacturing-scale bioreactor culture; and a computer configuredfor determining a culture parameter of the manufacturing-scalebioreactor culture, wherein the computer comprises: an input interfaceconfigured to receive information indicative of the Raman spectrumobtained from the probe; at least one processor programmed to evaluate amodel that relates the Raman spectrum to the culture parameter, whereinthe model is developed based on one or more test bioreactor cultures ofa smaller volume than the manufacturing-scale bioreactor culture, and anoutput interface configured to output a signal indicative of thedetermined culture parameter.
 26. The bioreactor system of claim 25,wherein the output comprises a feedback control signal for controllingoperation of a device for altering the culture parameter.
 27. Thebioreactor system of claim 26, wherein the device for altering theculture parameter is a pump or valve that controls flow, into or outfrom the bioreactor culture, of a medium comprising one or more culturecomponents.
 28. The bioreactor system of claim 25, wherein the volume ofthe manufacturing-scale bioreactor culture is in a range of 1000 L to4000 L.
 29. The bioreactor system of claim 25, wherein the volume of thetest bioreactor culture is in a range of 1 L to 400 L. 30-33. (canceled)34. The bioreactor system of claim 25, wherein the model is a partialleast squares model.
 35. The bioreactor system of claim 25, wherein theculture parameter is a level of glucose, glutamate, ammonia or lactatein the culture. 36-39. (canceled)
 40. The bioreactor system of claim 25,wherein the culture parameter is the osmolality of the culture. 41-43.(canceled)
 44. The bioreactor system of claim 25, wherein the probecomprises a Raman analyzer configured with a laser or other suitablelight source that operates at wavelengths in a range of 325 nm to 1064nm.
 45. (canceled)